update

.gitignore

@@ -2,10 +2,14 @@
 __pycache__/
 
 # Distribution / packaging
+release/
 build/
 dist/
-
-# Environments
+dist_nuitka/
+upperHost_stim/
+.vscode/
+#!upperHost_stim/MI_headless.py
+#!upperHost_stim/ssmvep_headless.py
 .env
 .venv
 env/
@@ -24,7 +28,8 @@ venv.bak/
 *.xlsx
 *.mat
 *.json
-
+*.txt
+*.pth
 
 # PyCharm
 #  JetBrains specific template is maintained in a separate repository that is not distributed with PyCharm itself

Decoder.py

@@ -1,6 +1,7 @@
 import ast
 import glob
 import os
+import sys
 import threading
 from datetime import datetime
 import multiprocessing as mp
@@ -10,58 +11,59 @@ import torch
 from queue import Empty
 from scipy import signal
 from torch.autograd import Variable
-from Device.SunnyLinker import SunnyLinker64
+# from Device.SunnyLinker import SunnyLinker64
 from SSMVEP.algorithm.tdca import TDCA
 from SSMVEP.algorithm.base import generate_cca_references
-from concentration.algorithm.calculate_focus import Calculate
-from blinkdetection.algorithm.eye_detection import blink_detection
+# from concentration.algorithm.calculate_focus import Calculate
+# from blinkdetection.algorithm.eye_detection import blink_detection
 from Zmq.zmqServer import zmqServer
 from Zmq.zmqClient import zmqClient
 from MI.Algorithm.conformer_2class import onlineTrain
 from PubLibrary.InifileHelper import IniRead
+from logs.log import algo_log
 from SSVEP.dwfbcca import FbccaDw
-from Tools.plot_MI_EEG import plotMain
+# from Tools.plot_MI_EEG import plotMain
 from collections import deque
+from Zmq.filterProcess import SlidingFilter
 
-class Decoder_main(threading.Thread, device_type):
-    def __init__(self, device_type=None):
+save_train_data = int(IniRead('system', 'save_train_data', 0))
+
+def get_root_path():
+    """
+    Nuitka 打包专用：获取程序根目录（.py 或 .exe 所在目录）
+    """
+    if getattr(sys, 'frozen', False):
+        # 打包后：返回 exe 所在目录
+        return os.path.dirname(sys.executable)
+    else:
+        # 开发时：返回 py 文件所在目录
+        return os.path.dirname(os.path.abspath(__file__))
+MODEL_FOLDER = "online_Models"
+
+
+class Decoder_main(threading.Thread):
+    def __init__(self, device_info=None):
         threading.Thread.__init__(self)
+        self.device_info = device_info
         self.Runing=True
         self.decoder = None
-
-        self.fs = 250  # 采样率
-        self.energy = 0  # 电量
-        self.status_code = 0  # 与采集设备通信的状态码，0为异常，1为正常
         self.decoder_class = None #解码器类别
-
         self.decodingSteps = 0  # 0=停止解码 1=预热 2=解码中 3=解码完成，发送解码结果
-        self.device_info = {
-            'device_type': None,
-            'sample_rate': None,
-            'channel_num': None,
-        }
 
-    def connect(self, device_type=None, device_host=None, device_port=None, upper_host=None, upper_port=None):
-        self.DeviceType = device_type if device_type is not None else int(IniRead('system', 'Device_type'))
-        _device_host = device_host if device_host is not None else str(IniRead('system', 'Device_Host'))
-        _device_port = device_port if device_port is not None else int(IniRead('system', 'Device_Port'))
-        _upper_host = upper_host if upper_host is not None else str(IniRead('system', 'Upper_Host'))
-        _upper_port = upper_port if upper_port is not None else int(IniRead('system', 'Upper_Port'))
 
-        if self.DeviceType == 1:
-            self.thread_data_server = SunnyLinker64(_device_host, _device_port, self.fs, 64, method='tcp')
-            self.thread_data_server.host = _device_host
-            self.thread_data_server.port = _device_port
+        self.zmqServer = zmqServer(device_info=self.device_info)
+        self.zmqServer.start()  # 启动ZMQ接收线程
         
-        self.thread_data_server.toUv = True
-        self.thread_data_server.start()
+        self.sliding_filter = SlidingFilter(
+            ring_buffer=self.zmqServer.filterBuffer,
+            n_chan=self.zmqServer.device_info['channel_nums'],
+            srate=self.zmqServer.device_info['sample_rate']
+        )
 
-        self.zmqServer = zmqServer()
-        self.zmqServer.start()
-
-        self.zmqClient = zmqClient(_upper_host, _upper_port)
-        self.zmqClient.set_zmq_server(self.zmqServer)
-        self.zmqClient.connect()
+        # 注册滤波结果回调（示例：打印数据形状）
+        self.sliding_filter.filter_result_callback = self.zmqServer.send_filtered_data
+        # 注册 beta_psd 广播回调，每秒通过 8099 端口发送给上位机
+        self.sliding_filter.set_beta_broadcast_callback(lambda v: self.zmqServer.broadcast_message('beta_psd', v))
 
     def is_valid_signal(self, data, threshold=1e5):  # 判断当前信号是否为有效信号
         # data: (chans, samples)
@@ -76,45 +78,44 @@ class Decoder_main(threading.Thread, device_type):
         :return:
         '''
         self.decoder_class = decoder_class
-        if decoder_class == 'ssvep' or decoder_class == 'pvs':
+        if self.decoder_class == 'ssvep' or self.decoder_class == 'pvs':
             self.n_chan = 8
-            self.thread_data_server.interval_inited = False
+            # self.thread_data_server.interval_inited = False
             DW_cost_method, self.DW_cost_tv = ast.literal_eval(IniRead('system', 'SSVEP_ThresholdValue'))
             self.ListFreq = self.zmqServer.targetFreqs
             self.num_target = len(self.ListFreq)
             if self.num_target == 0:
                 return
             # 初始化对象 二代算法
-            self.dw = FbccaDw(self.fs, self.num_target, self.n_chan, 5, 5,
+            self.dw = FbccaDw(self.device_info['sample_rate'], self.num_target, self.n_chan, 5, 5,
                               0.2, [2.0, 0.1], [8, 7], 50, DW_cost_method)
             #  frequence band
             self.dw.filterFrequenceBank()
             self.dw.setNotchFilterPara()
             self.calculateCount = 0
-            self.referenceData = self.dw.reference(self.ListFreq, int(50 * 0.2 * self.fs),
-                                                   5)
+            self.referenceData = self.dw.reference(self.ListFreq, int(50 * 0.2 * self.device_info['sample_rate']), 5)
             self.dw.filterInit()
             self.dw.onlineInit()  # 刺激闪烁的第1s重置 --在线数据采集时
 
         elif decoder_class == 'ssmvep':
-            self.thread_data_server.interval_init(decoder_class)
+            self.zmqServer.interval_init(decoder_class)
             self.n_chan = 8
-            self.interval_epoch = ast.literal_eval(IniRead('system', 'SSMVEP_IntervalEpoch'))
+            self.interval_epoch = ast.literal_eval(IniRead('system', 'SSMVEP_IntervalEpoch'))  # [0.2, 2.2]
             self.sample_length = round(self.interval_epoch[1] - self.interval_epoch[0], 6)  # 解码数据长度2s,# 精确到小数点后6位
             self.single_train = 10  # 单类别数量
             self.num_target = 2  # 分类目标数目
             self.list_freqs = np.array([8, 9])  # 刺激频率
             self.list_phase = np.array([0, 0])  # 相位
             self.tdca = TDCA(padding_len=5, n_components=1)
-            self.Yf = generate_cca_references(self.list_freqs, srate=self.fs, T=self.sample_length,
+            self.Yf = generate_cca_references(self.list_freqs, srate=self.device_info['sample_rate'], T=self.sample_length,
                                               phases=self.list_phase, n_harmonics=5)
             self.parameter_init(5,45)
 
         elif decoder_class == 'mi' or decoder_class == 'ma':
-            self.thread_data_server.interval_init(decoder_class)
+            self.zmqServer.interval_init(decoder_class)
             self.n_chan = 21
-            self.interval_epoch = ast.literal_eval(IniRead('system', 'MI_IntervalEpoch'))
-            self.sample_length = round(self.interval_epoch[1] - self.interval_epoch[0], 6)  # 解码数据长度2s,# 精确到小数点后6位
+            self.interval_epoch = ast.literal_eval(IniRead('system', 'MI_IntervalEpoch'))  # [0.5, 4.5]
+            self.sample_length = round(self.interval_epoch[1] - self.interval_epoch[0], 6)  # 解码数据长度4s,# 精确到小数点后6位
             self.single_train = 40  # 单类别数量
             self.num_target = 2  # 分类目标数目
 
@@ -126,7 +127,7 @@ class Decoder_main(threading.Thread, device_type):
         #     self.win_len = 10
         #     self.win_step = 1
         #     self.low_threshold, self.high_threshold = ast.literal_eval(IniRead('system', 'concentration_ThresholdValue'))
-        #     self.calculate = Calculate(self.low_threshold, self.high_threshold, self.fs, self.win_len)
+        #     self.calculate = Calculate(self.low_threshold, self.high_threshold, self.device_info['sample_rate'], self.win_len)
         #     self.interval_epoch = [0, 1]
         #     self.parameter_init(2, 40)
         #     # self.eegQueue moved to Calculate class
@@ -138,8 +139,8 @@ class Decoder_main(threading.Thread, device_type):
         #     self.total_samples = 0  # 总采样点数
         #     self.window_ms = 600  # 检测窗口大小 (ms)
         #     self.step_ms = 100  # 滑动步长 (ms)
-        #     self.window_samples = int(self.window_ms * self.fs / 1000)  # 150个样本点
-        #     self.step_samples = int(self.step_ms * self.fs / 1000)  # 25个样本点
+        #     self.window_samples = int(self.window_ms * self.device_info['sample_rate'] / 1000)  # 150个样本点
+        #     self.step_samples = int(self.step_ms * self.device_info['sample_rate'] / 1000)  # 25个样本点
         #     self.buffer_size = self.window_samples + self.step_samples * 5
         #     self.fp1_buffer = deque(maxlen=self.buffer_size)
         #     self.fp2_buffer = deque(maxlen=self.buffer_size)
@@ -153,11 +154,11 @@ class Decoder_main(threading.Thread, device_type):
         #     self.double_blink_events = []  # 连续眨眼事件记录
         #     self.last_double_blink_time = 0  # 上次检测到连续眨眼的时间戳
         #     self.blink_events = []
-        #     self.blink_b, self.blink_a = signal.butter(4, [self.l_freq / (self.fs / 2), self.h_freq / (self.fs / 2)], btype='band')
+        #     self.blink_b, self.blink_a = signal.butter(4, [self.l_freq / (self.device_info['sample_rate'] / 2), self.h_freq / (self.device_info['sample_rate'] / 2)], btype='band')
 
     def parameter_init(self,bandPass_low,bandPass_high):
-        self.interval_epoch = [int(i * self.fs) for i in self.interval_epoch]  # epoch截取信息
-        self.train_epoch = [int(self.interval_epoch[0]), int(self.interval_epoch[1] + 0.1 * self.fs)]  # 训练样本epoch
+        self.interval_epoch = [int(i * self.device_info['sample_rate']) for i in self.interval_epoch]  # epoch截取信息
+        self.train_epoch = [int(self.interval_epoch[0]), int(self.interval_epoch[1] + 0.1 * self.device_info['sample_rate'])]  # 训练样本epoch
         self.trainData = [] #训练数据
         self.trainLabel = [] #训练标签
         self.plotData = [] #报告分析数据
@@ -165,12 +166,12 @@ class Decoder_main(threading.Thread, device_type):
         self.currentLabel = -1 #刺激界面当前显示的训练标签
         self.train_started = False #是否开始训练模型
         self.load_model = False  # 调用模型是否完成的标志
-        self.b_notch, self.a_notch = signal.iirnotch(50 / (self.fs/2), 30)  # 50Hz工频陷波，250是采样率，30是质量因子
-        self.b_design = signal.firwin(65, [bandPass_low / (self.fs/2), bandPass_high / (self.fs/2)], pass_zero=False)  # 设计8-30Hz带通滤波器
-        fileName = 'Model_' + datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
-        filePath = './online_Models/'
+        self.b_notch, self.a_notch = signal.iirnotch(50 / (self.device_info['sample_rate']/2), 30)  # 50Hz工频陷波，250是采样率，30是质量因子
+        self.b_design = signal.firwin(65, [bandPass_low / (self.device_info['sample_rate']/2), bandPass_high / (self.device_info['sample_rate']/2)], pass_zero=False)  # 设计8-30Hz带通滤波器
+        filePath = os.path.join(get_root_path(), MODEL_FOLDER) + os.sep
         for old_pth in glob.glob(os.path.join(filePath, '*.pth')):
             os.remove(old_pth)
+        fileName = 'Model_' + datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
         self.modelPath = ''.join([filePath, fileName, '.pth'])
         self.mp_data_queue = mp.Queue()
         self.mp_result_queue = mp.Queue()
@@ -187,8 +188,13 @@ class Decoder_main(threading.Thread, device_type):
 
     def run(self):
         while self.Runing:
+            # 当滤波数据大于5秒时，启动滤波线程
+            if not self.sliding_filter.is_alive() and self.zmqServer.filterBuffer.GetDataLenCount() > self.device_info['sample_rate'] * 5:
+                algo_log("启动滤波线程", level="DEBUG")
+                self.sliding_filter.start()
+            
             if self.zmqServer.decoder_switch or self.zmqServer.changeTarget:
-                print(f"Decoder_class Switch Detected: {self.zmqServer.decoder_class}")
+                algo_log(f"Decoder_class Switch Detected: {self.zmqServer.decoder_class}", level="DEBUG")
                 self.zmqServer.decoder_switch = False
                 self.zmqServer.changeTarget = False
                 self.reset_state()  # 切换前先统一清理旧状态
@@ -196,150 +202,96 @@ class Decoder_main(threading.Thread, device_type):
 
             # 同步信息
             if self.zmqServer.state_mode == 'sync':
-                self.zmqClient.send_to_all('sync', self.zmqClient.state)
+                # self.zmqClient.send_to_all('sync', self.zmqClient.state)
                 self.zmqServer.state_mode = 'rest'
-            # 状态异常，报告上位机
-            if self.status_code != self.thread_data_server.status_code:
-                self.status_code = self.thread_data_server.status_code
-                self.zmqClient.send_to_all('status_code', int(self.status_code))
-                print('status code')
 
-            # 返回电量
-            if self.energy != self.thread_data_server.energy:
-                self.energy = self.thread_data_server.energy
-                self.zmqClient.send_to_all('energy', int(self.energy))
-                print('energy')
-
-            if self.zmqServer.open_Impedance == True:  # 开启阻抗检测功能，仅运行一次
-                self.thread_data_server.Impedance(True)
-                print('Impedance')
-                self.zmqServer.open_Impedance = -1
-            elif self.zmqServer.open_Impedance == False:
-                self.thread_data_server.Impedance(False)
-                self.zmqServer.open_Impedance = -1
-
-            if self.zmqServer.get_Impedance:  # 返回阻抗值
-                # print(self.zmqServer.get_Impedance)
-                # print(self.thread_data_server.GetDataLenCount())
-                if self.thread_data_server.GetDataLenCount() > 250:
-                    Impe_data = self.thread_data_server.getData(250)
-                    # 计算阻抗
-                    imps = self.thread_data_server.getImpedance(Impe_data,self.zmqServer.decoder_class)
-                    self.zmqClient.send_to_all('impedance', imps.tolist())
-                else:
-                    pass
-            if self.zmqServer.getReport:  #返回训练报告内容
-                self.zmqServer.getReport = False
-                allData = np.array(self.plotData)
-                allLabel = np.array(self.plotLabel) + 1
-                nTrials = min(len(allLabel),len(allData))
-                if nTrials < 30:
-                    self.zmqClient.send_to_all('miReport',0)
-                else:
-                    allData = allData[:nTrials]
-                    allLabel = allLabel[:nTrials]
-                    ch_names = ['FC3', 'FC1', 'FCZ', 'FC2', 'FC4', 'C5', 'C3', 'C1', 'CZ', 'C2', 'C4', 'C6', 'CP3', 'CP1',
-                                'CP2', 'CP4', 'P3', 'P1', 'PZ', 'P2', 'P4']
-                    compare_names = ['C3', 'CZ', 'C4']
-                    miReport = plotMain(ch_names=ch_names,compare_names=compare_names,Data=allData,labels=allLabel,MI_label=1,Rest_label=2,
-                             fs=self.fs)
-                    self.zmqClient.send_to_all('miReport',miReport)
-
-
-            # --- 取数优先：先执行 decoder（消费环形缓冲），再处理 plot/report 等重负载 ---
             try:
+                if self.zmqServer.open_Impedance:
+                    time.sleep(0.005)
+                    continue
                 if self.decoder_class == 'ssvep' or self.decoder_class == 'pvs':
                     self.decoder_SSVEP()
                 elif self.decoder_class == 'ssmvep':
                     self.decoder_SSMVEP()
                 elif self.decoder_class == 'mi':
                     self.decoder_MI()
-                elif self.decoder_class == 'concentration':
-                    self.decoder_concentration()
-                elif self.decoder_class == 'blink':
-                    self.decoder_blink()
                 else:
-                    if self.zmqServer.get_Impedance == False:  # 非阻抗检测状态
-                        if self.thread_data_server.GetDataLenCount() < 25:
+                    if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25:
                         time.sleep(0.005)
-                            continue;
-                        self.thread_data_server.getData(25)
+                        continue
+                    self.zmqServer.paradigmBuffer.getData(25)
             except Exception as e:
-                print(f"Decoder Loop Error: {e}")
-                import traceback
-                traceback.print_exc()
+                algo_log(f"Decoder Loop Error: {e}")
                 time.sleep(0.1)  # Prevent CPU spin if error is persistent
 
     def decoder_SSVEP(self):
         if self.zmqServer.StartDecode:
             self.zmqServer.StartDecode = False
             self.decodingSteps = 1
-            self.thread_data_server.ResetAll()
-            print('启动预测')
-        if self.thread_data_server.GetDataLenCount() < 50:
+            self.zmqServer.paradigmBuffer.resetAllPara()
+            algo_log('启动SSVEP预测', level="DEBUG")
+        if self.zmqServer.paradigmBuffer.GetDataLenCount() < 50:
             time.sleep(0.005)
             return
-        if self.zmqServer.get_Impedance != False:  # 阻抗检测状态不解码
+        if self.zmqServer.open_Impedance:  # 阻抗检测状态不解码
             return
-        data = self.thread_data_server.getDataViaSSVEP(50)
+        data = self.zmqServer.paradigmBuffer.getDataViaSSVEP(50)
+        # algo_log(f"SSVEP取出的：{data.shape}, data = {data[:20]}", level="DEBUG")
         data = data[:self.n_chan, :]
         if self.decodingSteps == 1 and hasattr(self,'dw'):  # 开始预热
             self.dw.onlineInit()  # 刺激闪烁的第1s重置 --在线数据采集时
             self.dw.warmFilter(data)  # 预热
             self.decodingSteps = 2
-            print('预热数据完成。开始预测')
+            algo_log('SSVEP预热数据完成。开始预测', level="DEBUG")
             return
         if self.decodingSteps == 2 and hasattr(self,'dw'):  # 解码中
             choosenNum = self.dw.fbccaDWMW(data, self.referenceData, self.DW_cost_tv, self.calculateCount)
             self.calculateCount += 1
             if choosenNum != -1 and self.is_valid_signal(data):
                 self.decodingSteps = 3
-                print('预测结果：' + str(choosenNum) + ',计算次数：' + str(self.calculateCount))
+                algo_log('SSVEP预测结果：' + str(choosenNum) + ',计算次数：' + str(self.calculateCount), level="DEBUG")
                 self.calculateCount = 0
         if self.decodingSteps == 3:  # 发送解码后的信息
-            self.zmqClient.send_to_all('result', int(choosenNum))
+            self.zmqServer.broadcast_message('result', int(choosenNum))
             self.decodingSteps = 0
-            print('发送给界面完成。')
+            algo_log('SSVEP发送给界面完成。', level="DEBUG")
 
     def decoder_SSMVEP(self):
         '''模型训练'''
         if self.load_model == False and all(
-                self.trainLabel.count(i) >= self.single_train for i in range(len(self.list_freqs))):  # 模型尚未训练完成
+                self.trainLabel.count(i) >= self.single_train for i in [1, 2]):  # 模型尚未训练完成
             self.trainData = np.array(self.trainData)
             self.trainLabel = np.array(self.trainLabel)
-            print(np.shape(self.trainData), (self.trainLabel))
-            # 保存多个数组到文件
-            # np.savez('20250520_yy.npz', array1=self.trainData, array2=self.trainLabel)
-            # self.decoder = self.fbtdca.fit(self.trainData, self.trainLabel, Yf=self.Yf)
+            algo_log(f"开始SSMVEP模型训练，数据形状：{np.shape(self.trainData)}，标签形状：{self.trainLabel.shape}", level="DEBUG")
+            if save_train_data == 1:
+                now_str = datetime.now().strftime("%Y%m%d_%H%M%S")
+                save_path = f"{now_str}.npz"
+                np.savez(save_path, array1=self.trainData, array2=self.trainLabel)
             self.decoder = self.tdca.fit(self.trainData, self.trainLabel, Yf=self.Yf)
             now = datetime.now()
             formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
-            print('模型训练完成', formatted_time)
+            algo_log(f"SSMVEP模型训练完成，时间：{formatted_time}", level="DEBUG")
             self.load_model = True
-            self.zmqClient.send_to_all('paradigm', 1)
+            self.zmqServer.broadcast_message('paradigm', 1)
 
         '''训练阶段采集数据'''
         if self.zmqServer.state_mode == 'train':  # 训练状态
-            if self.zmqServer.StartTrain:
+            if self.zmqServer.epoch_finished  and self.zmqServer.paradigmBuffer.GetDataLenCount() >= \
+                    self.train_epoch[1] + self.zmqServer.event_inner_idx:
                 self.currentLabel = self.zmqServer.currentLabel
-                self.zmqServer.StartTrain = False
-            if self.thread_data_server.epoch_finished == False or self.thread_data_server.GetDataLenCount() < \
-                    self.train_epoch[1] \
-                    + self.thread_data_server.event_inner_idx:
-                time.sleep(0.0001)
-                return
-            print('训练队列数据：', self.thread_data_server.GetDataLenCount())
-            trainTrial = self.thread_data_server.get_SSMVEPData()  # 取出所有数据
-            print('取出的： ', trainTrial.shape, 'event: ', trainTrial[-2, self.thread_data_server.event_inner_idx])
+                trainTrial = self.zmqServer.paradigmBuffer.get_SSMVEPData()  # 取出所有数据
+                algo_log(f"取出的：{trainTrial.shape}，event：{trainTrial[-2, self.zmqServer.event_inner_idx]}", level="DEBUG")
                 trainTrial = self.preprocess(trainTrial[:self.n_chan, :])  # 预处理
-            trainTrial = trainTrial[:, self.thread_data_server.event_inner_idx + self.train_epoch[
-                0]:self.thread_data_server.event_inner_idx + self.train_epoch[1]]
-            print('trial: ', self.thread_data_server.event_inner_idx, self.train_epoch[0], self.train_epoch[1])
+                trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.train_epoch[
+                    0]:self.zmqServer.event_inner_idx + self.train_epoch[1]]
                 if trainTrial.shape[1] == (self.train_epoch[1] - self.train_epoch[0]) and isinstance(
                         self.trainLabel, list) \
                         and self.trainLabel.count(self.currentLabel) < self.single_train:
                     self.trainData.append(trainTrial)
                     self.trainLabel.append(self.currentLabel)
+            else:
+                time.sleep(0.0001)
+                return
 
         elif self.zmqServer.state_mode == 'predict':  # 测试状态
             if self.load_model == False:  # 模型尚未训练完成
@@ -350,45 +302,47 @@ class Decoder_main(threading.Thread, device_type):
                     self.zmqServer.StartDecode = False
                     now = datetime.now()
                     formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
-                    print('启动预测  ', formatted_time)
-
-                if self.thread_data_server.epoch_finished == False or self.thread_data_server.GetDataLenCount() < \
+                    algo_log(f"SSMVEP模型启动预测  {formatted_time}", level="DEBUG")
+                if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
                         self.interval_epoch[1] \
-                        + self.thread_data_server.event_inner_idx:
+                        + self.zmqServer.event_inner_idx:
+                    # algo_log(f"SSMVEP模型启动预测  {self.zmqServer.epoch_finished}", level="DEBUG")
                     time.sleep(0.0001)
                     return
-                data = self.thread_data_server.get_SSMVEPData()  # 读取全部数据
-                print('取出的： ', data.shape, 'event: ', data[-2, self.thread_data_server.event_inner_idx])
+                data = self.zmqServer.paradigmBuffer.get_SSMVEPData()  # 读取全部数据
+                algo_log(f"取出的：{data.shape}, event: {data[-2, self.zmqServer.event_inner_idx]}", level="DEBUG")
                 data = self.preprocess(data[:self.n_chan, :])  # 预处理
                 data = data[:,
-                       self.thread_data_server.event_inner_idx + self.interval_epoch[
-                           0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]]
+                       self.zmqServer.event_inner_idx + self.interval_epoch[
+                           0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]
                 pad_eeg_test = np.zeros(
-                    (data.shape[0], int((self.sample_length + 0.1) * self.fs)))
-                pad_eeg_test[:, :int(self.sample_length * self.fs)] = data
+                    (data.shape[0], int((self.sample_length + 0.1) * self.device_info['sample_rate'])))
+                pad_eeg_test[:, :int(self.sample_length * self.device_info['sample_rate'])] = data
                 choosenNum, features_2 = self.decoder.predict(pad_eeg_test)
                 if isinstance(choosenNum, np.ndarray):
                     choosenNum = choosenNum[0]
-                print('结果：', choosenNum, 'rho: ', sorted(features_2[0]),
-                      sorted(features_2[0])[-1] - sorted(features_2[0])[-2])
-                self.zmqClient.send_to_all('result', int(choosenNum))
-                print('发送给界面完成。')
+                algo_log(f"结果：{choosenNum}, rho: {sorted(features_2[0])[-1] - sorted(features_2[0])[-2]}", level="DEBUG")
+                self.zmqServer.broadcast_message('result', int(choosenNum))
+                algo_log("SSMVEP发送给界面完成。", level="DEBUG")
         else:  # 休息状态
-            if self.zmqServer.get_Impedance == False:  # 非阻抗检测状态
-                if self.thread_data_server.GetDataLenCount() < 25:
+            if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25:
                 time.sleep(0.005)
                 return 
-                self.thread_data_server.getData(25)
+            self.zmqServer.paradigmBuffer.getData(25)
 
     def decoder_MI(self):
         '''模型训练'''
         if self.train_started == False and all(
-                self.trainLabel.count(i) >= self.single_train for i in range(self.num_target)):  # 模型尚未训练
-            self.zmqClient.send_to_all('paradigm', 2)  # 模型训练前，训练集采集完毕，通知上位机
+                self.trainLabel.count(i) >= self.single_train for i in [1, 2]):  # 模型尚未训练
+            self.zmqServer.broadcast_message('paradigm', 2)  # 模型训练前，训练集采集完毕，通知上位机
             self.train_started = True
             self.trainData = np.array(self.trainData)
-            self.trainLabel = np.array(self.trainLabel) + 1
-            # print('训练集：',np.shape(self.trainData), (self.trainLabel))
+            self.trainLabel = np.array(self.trainLabel)
+            algo_log(f"MI开始训练，训练集：{np.shape(self.trainData)}，标签shape：{np.shape(self.trainLabel)}", level="DEBUG")
+            if save_train_data == 1:
+                now_str = datetime.now().strftime("%Y%m%d_%H%M%S")
+                save_path = f"{now_str}.npz"
+                np.savez(save_path, array1=self.trainData, array2=self.trainLabel)
             p = mp.Process(target=onlineTrain, args=(self.mp_data_queue, self.mp_result_queue))  # 开启子进程，训练模型
             p.start()
             self.mp_data_queue.put({'data': self.trainData, 'label': self.trainLabel, 'modelPath': self.modelPath,
@@ -399,7 +353,7 @@ class Decoder_main(threading.Thread, device_type):
             try:
                 result = self.mp_result_queue.get_nowait()
                 if result['status'] == 'success':
-                    print("模型训练完成，加载新模型")
+                    algo_log("MI模型训练完成，加载新模型", level="DEBUG")
                     # 调用模型
                     self.model = torch.load(self.modelPath, weights_only=False)
                     self.model.eval()
@@ -410,63 +364,61 @@ class Decoder_main(threading.Thread, device_type):
                     with torch.no_grad():
                         _ = self.model(warmup_data)
                     self.load_model = True
-                    self.zmqClient.send_to_all('paradigm', 1)  # 模型调用完毕，通知上位机
+                    self.zmqServer.broadcast_message('paradigm', 1)  # 模型调用完毕，通知上位机
                 else:
-                    print("训练失败:", result['msg'])
+                    algo_log("MI训练失败: " + result['msg'], level="DEBUG")
             except Empty:
                 pass  # 还没完成
             except Exception as e:
-                print('模型调用失败: ', e)
+                algo_log("MI模型训练失败: " + str(e), level="DEBUG")
 
         '''训练阶段采集数据'''
         if self.zmqServer.state_mode == 'train' and self.train_started == False:  # 训练状态
-            if self.zmqServer.StartTrain:
-                self.currentLabel = self.zmqServer.currentLabel
-                self.zmqServer.StartTrain = False
-            if self.thread_data_server.epoch_finished == False or self.thread_data_server.GetDataLenCount() < \
-                    self.interval_epoch[1] \
-                    + self.thread_data_server.event_inner_idx:
-                time.sleep(0.0001)
-                return
-            print('训练队列数据：', self.thread_data_server.GetDataLenCount())
-            originalTrial = self.thread_data_server.get_MIData()  # 取出MI导联数据
-            print('取出的： ', originalTrial.shape, 'event: ', originalTrial[-2, self.thread_data_server.event_inner_idx])
+            if self.zmqServer.epoch_finished and self.zmqServer.paradigmBuffer.GetDataLenCount() >= \
+                    self.zmqServer.train_epoch[1] + self.zmqServer.event_inner_idx:
+                self.currentLabel = self.zmqServer.currentLabel  # 同步当前标签
+                algo_log(f"训练队列数据：{self.zmqServer.paradigmBuffer.GetDataLenCount()}", level="DEBUG")
+                originalTrial = self.zmqServer.paradigmBuffer.get_MIData()  # 取出MI导联数据
+                algo_log(f"取出的：{originalTrial.shape},event: {originalTrial[-2, self.zmqServer.event_inner_idx]}", level="DEBUG")
                 trainTrial = self.preprocess(originalTrial[:self.n_chan, :])  # 预处理
-            trainTrial = trainTrial[:, self.thread_data_server.event_inner_idx + self.interval_epoch[
-                0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]]
-            print('trial: ', self.thread_data_server.event_inner_idx, self.interval_epoch[0], self.interval_epoch[1])
+                trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.interval_epoch[
+                    0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]
+                # algo_log(f"trial: {self.zmqServer.event_inner_idx},{self.interval_epoch[0]},{self.interval_epoch[1]}", level="DEBUG")
                 if trainTrial.shape[1] == (self.interval_epoch[1] - self.interval_epoch[0]) and isinstance(self.trainLabel,
                                                                                                         list) \
                         and self.trainLabel.count(self.currentLabel) < self.single_train:
                     self.trainData.append(trainTrial)
                     self.trainLabel.append(self.currentLabel)
-                print('训练集：', np.shape(self.trainData))
-                self.plotData.append(originalTrial[:self.n_chan, self.thread_data_server.event_inner_idx + self.interval_epoch[
-                0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]])
+                    algo_log(f"训练集：{np.shape(self.trainData)}", level="DEBUG")
+                    self.plotData.append(originalTrial[:self.n_chan, self.zmqServer.event_inner_idx + self.interval_epoch[
+                    0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]])
                     self.plotLabel.append(self.currentLabel)
+            else:
+                time.sleep(0.0001)
+                return
 
         elif self.zmqServer.state_mode == 'predict' and self.load_model == True:  # 测试状态
             if self.zmqServer.StartDecode:
                 self.zmqServer.StartDecode = False
                 now = datetime.now()
                 formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
-                print('启动预测  ', formatted_time)
+                algo_log(f"MI启动预测  {formatted_time}", level="DEBUG")
 
-            if self.thread_data_server.epoch_finished == False or self.thread_data_server.GetDataLenCount() < \
+            if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
                     self.interval_epoch[1] \
-                    + self.thread_data_server.event_inner_idx:
+                    + self.zmqServer.event_inner_idx:
                 time.sleep(0.0001)
                 return
-            originalData = self.thread_data_server.get_MIData()  # 读取全部数据
-            print('取出的： ', originalData.shape, 'event: ', originalData[-2, self.thread_data_server.event_inner_idx])
+            originalData = self.zmqServer.paradigmBuffer.get_MIData()  # 读取全部数据
+            algo_log(f"取出的：{originalData.shape},event: {originalData[-2, self.zmqServer.event_inner_idx]}", level="DEBUG")
             start = time.time()
             data = self.preprocess(originalData[:self.n_chan, :])  # 预处理
             data = data[:,
-                   self.thread_data_server.event_inner_idx + self.interval_epoch[
-                       0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]]
+                   self.zmqServer.event_inner_idx + self.interval_epoch[
+                       0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]
             self.plotData.append(
-                originalData[:self.n_chan, self.thread_data_server.event_inner_idx + self.interval_epoch[
-                    0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]])
+                originalData[:self.n_chan, self.zmqServer.event_inner_idx + self.interval_epoch[
+                    0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]])
 
             test_data = data[np.newaxis, np.newaxis, :, :]
             test_data = torch.from_numpy(test_data)
@@ -475,134 +427,40 @@ class Decoder_main(threading.Thread, device_type):
                 Cls = self.model(test_data)
                 y_pred = torch.max(Cls, 1)[1]
                 self.plotLabel.append(int(y_pred.item()))
-                print('运动意图识别: ', y_pred)
-            self.zmqClient.send_to_all('result', int(y_pred.item()))
+                algo_log(f"MI运动意图识别: {y_pred}")
+            self.zmqServer.broadcast_message('result', int(y_pred.item()))
             end = time.time()
-            print(f'发送给界面完成,耗时{end - start:.3f}s。')
+            algo_log(f'MI发送给界面完成,耗时{end - start:.3f}s。')
         else:  # 休息状态
-            if self.zmqServer.get_Impedance == False:  # 非阻抗检测状态
-                if self.thread_data_server.GetDataLenCount() < 25:
+            if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25:
                 time.sleep(0.005)
                 return
-                self.thread_data_server.getData(25)
+            self.zmqServer.paradigmBuffer.getData(25)
 
-    def decoder_concentration(self):
-        if self.zmqServer.state_mode == 'predict':
-            if self.zmqServer.StartDecode:
-                self.zmqServer.StartDecode = False
-                self.thread_data_server.ResetAll()
-                now = datetime.now()
-                formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
-                print('启动专注力预测  ', formatted_time)
-            if self.thread_data_server.GetDataLenCount() < int(self.win_step * self.fs):  # 每win_step得出一次结果
-                time.sleep(0.005)
-                return
-            if self.zmqServer.get_Impedance != False:  # 阻抗检测状态不解码
-                return
-            data = self.thread_data_server.get_concentrateData(int(self.win_step * self.fs))  # 修改每次读取的数据
-            result = self.calculate.queueOpt(data)
-            if result is not None:
-                self.zmqClient.send_to_all('result', int(result))
-        else:  # 休息状态
-            if self.zmqServer.get_Impedance == False:  # 非阻抗检测状态
-                if self.thread_data_server.GetDataLenCount() < 25:
-                    time.sleep(0.005)
-                    return
-                self.thread_data_server.getData(25)
+    # def decoder_concentration(self):
+    #     if self.zmqServer.state_mode == 'predict':
+    #         if self.zmqServer.StartDecode:
+    #             self.zmqServer.StartDecode = False
+    #             self.thread_data_server.ResetAll()
+    #             now = datetime.now()
+    #             formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
+    #             print('启动专注力预测  ', formatted_time)
+    #         if self.thread_data_server.GetDataLenCount() < int(self.win_step * self.device_info['sample_rate']):  # 每win_step得出一次结果
+    #             time.sleep(0.005)
+    #             return
+    #         if self.zmqServer.get_Impedance != False:  # 阻抗检测状态不解码
+    #             return
+    #         data = self.thread_data_server.get_concentrateData(int(self.win_step * self.device_info['sample_rate']))  # 修改每次读取的数据
+    #         result = self.calculate.queueOpt(data)
+    #         if result is not None:
+    #             self.zmqClient.send_to_all('result', int(result))
+    #     else:  # 休息状态
+    #         if self.zmqServer.get_Impedance == False:  # 非阻抗检测状态
+    #             if self.thread_data_server.GetDataLenCount() < 25:
+    #                 time.sleep(0.005)
+    #                 return
+    #             self.thread_data_server.getData(25)
 
-#### Blink detection #####
-    def check_double_blink(self, current_time):
-        """
-        检查是否检测到连续两次眨眼
-        @param current_time: 当前眨眼时间戳
-        @return: True表示检测到连续两次眨眼
-        """
-        if len(self.blink_timestamps) < 2:
-            return False
-
-        # 检查是否在去抖期内
-        if self.last_double_blink_time > 0:
-            time_since_last_double_blink = current_time - self.last_double_blink_time
-            if time_since_last_double_blink < self.double_blink_jitter:
-                return False  # 在去抖期内，忽略连续眨眼检测
-        last_time = self.blink_timestamps[-1]  # 当前眨眼
-        prev_time = self.blink_timestamps[-2]  # 上次眨眼
-
-        interval = last_time - prev_time
-        if interval <= self.double_blink_interval:
-            return True
-
-        return False
-
-    def process_blink_detection(self):
-        """
-        在缓冲区数据上执行,单次眨眼检测
-        """
-        if len(self.fp1_buffer) < self.window_samples:
-            return
-
-        fp1_data = np.array(list(self.fp1_buffer)[-self.window_samples:])
-        fp2_data = np.array(list(self.fp2_buffer)[-self.window_samples:])
-        # 计算FP1和FP2的平均
-        fp12_mean = (fp1_data + fp2_data) / 2.0
-        # 带通滤波
-        try:
-            fp12_filtered = signal.filtfilt(self.blink_b, self.blink_a, fp12_mean)
-        except Exception as e:
-            print(f"Filter error: {e}")
-            return
-        F = np.diff(fp12_filtered)
-        if len(F) < 3:
-            return
-        b, d, e = blink_detection(F, self.fs, self.Dmin, self.Dmax, self.EMin, self.EMax)
-
-        if b == 1:
-            samples_since_last = self.total_samples - self.last_blink_time
-            time_since_last_ms = (samples_since_last / self.fs) * 1000
-            if time_since_last_ms >= self.jitterwin:  # self.jitterwin  单次眨眼去抖  using time_since_last_ms
-                self.blink_count += 1
-                self.last_blink_time = self.total_samples
-                current_time = time.time()
-                self.blink_timestamps.append(current_time)
-                blink_event = {
-                    'count': self.blink_count,
-                    'time': current_time,
-                    'sample_index': self.total_samples,
-                    'duration_ms': d,
-                    'energy': e
-                }
-                self.blink_events.append(blink_event)
-                self.zmqClient.send_to_all('result', 1)  # 检测到眨眼信号，通知上位机
-                if self.check_double_blink(current_time):
-                    self.double_blink_count += 1
-                    interval = self.blink_timestamps[-1] - self.blink_timestamps[-2]
-                    double_blink_event = {
-                        'double_blink_count': self.double_blink_count,
-                        'blink1_time': self.blink_timestamps[-2],
-                        'blink2_time': self.blink_timestamps[-1],
-                        'interval': interval
-                    }
-                    self.double_blink_events.append(double_blink_event)
-                    self.last_double_blink_time = current_time
-                    self.zmqClient.send_to_all('result', 2) # 发送双次眨眼事件
-
-    def decoder_blink(self):
-        if self.thread_data_server.GetDataLenCount() < 50:
-            time.sleep(0.005)
-            return
-        if self.zmqServer.get_Impedance == False:  # 非阻抗检测状态
-            data = self.thread_data_server.get_blinkData(50)
-            fp1_data = data[0, :]  # ch1 (相当于FP1)
-            fp2_data = data[1, :]  # ch2 (相当于FP2)
-            for i in range(len(fp1_data)):
-                self.fp1_buffer.append(fp1_data[i])
-                self.fp2_buffer.append(fp2_data[i])
-                self.total_samples += 1
-                self.sample_counter += 1
-
-                if self.sample_counter >= self.step_samples:
-                    self.process_blink_detection()
-                    self.sample_counter = 0
 
     def stop(self):
         '''
@@ -610,12 +468,13 @@ class Decoder_main(threading.Thread, device_type):
         @return:
         '''
         self.zmqServer.stop()
+        self.sliding_filter.stop()
         self.Runing=False
 
     def reset_state(self):
         """清空解码器状态和缓存数据"""
         # 重置设备层缓存
-        self.thread_data_server.reset_state()
+        self.zmqServer.reset_state()
 
         # 重置解码状态
         self.decodingSteps = 0

MI/Algorithm/conformer_2class.py

@@ -34,7 +34,7 @@ cudnn.benchmark = True
 cudnn.deterministic = True
 from sklearn.model_selection import train_test_split
 # writer = SummaryWriter('./TensorBoardX/')
-
+from logs.log import algo_log
 
 # Convolution module
 # use conv to capture local features, instead of postion embedding.
@@ -82,7 +82,7 @@ class MultiHeadAttention(nn.Module):
         values = rearrange(self.values(x), "b n (h d) -> b h n d", h=self.num_heads)
         energy = torch.einsum('bhqd, bhkd -> bhqk', queries, keys)  
         if mask is not None:
-            fill_value = torch.finfo(torch.float32).min
+            fill_value = torch.finfo(torch.float64).min
             energy.mask_fill(~mask, fill_value)
 
         scaling = self.emb_size ** (1 / 2)
@@ -318,11 +318,7 @@ class ExP():
                 train_pred = torch.max(outputs, 1)[1]
                 train_acc = float((train_pred == label).cpu().numpy().astype(int).sum()) / float(label.size(0))
 
-                print('Epoch:', e,
-                      '  Train loss: %.6f' % loss.detach().cpu().numpy(),
-                      '  Test loss: %.6f' % loss_test.detach().cpu().numpy(),
-                      '  Train accuracy %.6f' % train_acc,
-                      '  Test accuracy is %.6f' % acc)
+                algo_log(f"Epoch = {e}, Train loss = {loss.detach().cpu().numpy():.6f}, Test loss = {loss_test.detach().cpu().numpy():.6f}, Train accuracy = {train_acc:.6f}, Test accuracy = {acc:.6f}", level="debug")
 
                 self.log_write.write(str(e) + "    " + str(acc) + "\n")
                 num = num + 1
@@ -335,8 +331,8 @@ class ExP():
 
         torch.save(self.model, model_path)
         averAcc = averAcc / num
-        print('The average accuracy is:', averAcc)
-        print('The best accuracy is:', bestAcc)
+        algo_log(f"The average accuracy is: {averAcc}", level="debug")
+        algo_log(f"The best accuracy is: {bestAcc}", level="debug")
         self.log_write.write('The average accuracy is: ' + str(averAcc) + "\n")
         self.log_write.write('The best accuracy is: ' + str(bestAcc) + "\n")
 
@@ -346,10 +342,10 @@ class ExP():
 
 def onlineTrain(data_queue,result_queue):
     import torch
-    print(f"[DEBUG] torch.__version__ = {torch.__version__}")
-    print(f"[DEBUG] torch.cuda.is_available() = {torch.cuda.is_available()}")
+    algo_log(f"[DEBUG] torch.__version__ = {torch.__version__}", level="debug")
+    algo_log(f"[DEBUG] torch.cuda.is_available() = {torch.cuda.is_available()}", level="debug")
     if torch.cuda.is_available():
-        print(f"[DEBUG] GPU = {torch.cuda.get_device_name(0)}")
+        algo_log(f"[DEBUG] GPU = {torch.cuda.get_device_name(0)}", level="debug")
     try:
         starttime = datetime.datetime.now()
 
@@ -366,12 +362,13 @@ def onlineTrain(data_queue,result_queue):
         data = data_queue.get(timeout=30)
         all_data, all_label,model_path,n_chan = data['data'], data['label'],data['modelPath'],data['n_chan']
         exp = ExP(n_chan)
-        print('训练参数： ',np.shape(all_data),np.shape(all_label),model_path)
+        algo_log(f"训练参数： {np.shape(all_data)}, {np.shape(all_label)}, {model_path}", level="debug")
         bestAcc, averAcc, Y_true, Y_pred = exp.train(all_data,all_label,model_path)
-        print('THE BEST ACCURACY IS ' + str(bestAcc))
+        algo_log(f"THE BEST ACCURACY IS {str(bestAcc)}", level="debug")
 
         endtime = datetime.datetime.now()
-        print('train duration: ',str(endtime - starttime))
+        algo_log(f"train duration: {endtime - starttime}", level="debug")
+        
 
         # 将模型或参数传回
         result_queue.put({
@@ -387,7 +384,7 @@ def offlineTrain(all_data,all_label,modelPath):
 
     # seed_n = np.random.randint(2025)
     seed_n = 1877
-    print('seed is ' + str(seed_n))
+    algo_log(f"seed is {seed_n}", level="debug")
     random.seed(seed_n)
     np.random.seed(seed_n)
     torch.manual_seed(seed_n)
@@ -397,13 +394,12 @@ def offlineTrain(all_data,all_label,modelPath):
     exp = ExP()
 
     bestAcc, averAcc, Y_true, Y_pred = exp.train(all_data,all_label,modelPath)
-    print('THE BEST ACCURACY IS ' + str(bestAcc))
+    algo_log('THE BEST ACCURACY IS ' + str(bestAcc), level="debug")
 
     endtime = datetime.datetime.now()
-    print('train duration: ',str(endtime - starttime))
+    algo_log(f"train duration: {endtime - starttime}", level="debug")
 
 
 
 if __name__ == "__main__":
-    print(time.asctime(time.localtime(time.time())))
-    print(time.asctime(time.localtime(time.time())))
+    algo_log(f"[DEBUG] time.asctime(time.localtime(time.time())) = {time.asctime(time.localtime(time.time()))}", level="debug")

MI/Algorithm/conformer_2class_cpu.py

@@ -22,6 +22,7 @@ from einops import rearrange
 from einops.layers.torch import Rearrange, Reduce
 from torch.backends import cudnn
 from sklearn.model_selection import train_test_split
+from logs.log import algo_log
 # writer = SummaryWriter('./TensorBoardX/')
 
 
@@ -71,7 +72,7 @@ class MultiHeadAttention(nn.Module):
         values = rearrange(self.values(x), "b n (h d) -> b h n d", h=self.num_heads)
         energy = torch.einsum('bhqd, bhkd -> bhqk', queries, keys)  
         if mask is not None:
-            fill_value = torch.finfo(torch.float32).min
+            fill_value = torch.finfo(torch.float64).min
             energy.mask_fill(~mask, fill_value)
 
         scaling = self.emb_size ** (1 / 2)
@@ -190,7 +191,7 @@ class ExP():
         # 自动选择设备：有 GPU 用 GPU，否则用 CPU
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         # self.device = torch.device("cpu")
-        print(f"Using device: {self.device}")
+        algo_log(f"Using device: {self.device}", level="debug")
 
         # 定义张量类型（不再强制使用 cuda）
         self.Tensor = torch.FloatTensor

README.md

@@ -13,5 +13,25 @@ Debug_64ch_Decoder_Optimize is an updated version that fixes several issues and
 6. decoder class切换问题
 7. decoder_class切换时，数据重置、各类参数重置
 
-# update
-2026年6月5日13:55:34
+# realease log
+- 2026年6月11日11:29:17 打包第一版，包名runDecoder.dist_v0.0.0_beta_20260611.7z
+- 2026年6月11日12:00:00 打包第二版，包名runDecoder.dist_v0.0.0_beta_20260611.7z
+    - 修复上位机先发decoder_class, 后发open_impedence 带来decoder_main thread 阻塞问题
+
+- 2026年6月12日15:05:47 runDecoder.dist_v0.0.2_beta_20260612
+    - 优化filter读数精度
+
+#  常用命令
+source activate 3in1Py310
+python runDecoder.py
+python datamock.py
+python ZeroMQClient_mock.py
+python filter_test.py
+python upperHost_stimmock/MI_headless.py
+
+# 打包命令
+./nuitka_3in1_package.sh
+
+# TODO
+1. mvep是否要把list freq 开放到config
+2. 滤波器参数 放到config文件

SSVEP/dwfbcca.py

@@ -12,16 +12,17 @@ from scipy.io import loadmat
 from scipy.linalg import qr
 from scipy.signal import filtfilt, lfilter
 # from numpy.linalg import _umath_linalg
+from logs.log import algo_log
 
 
 class FbccaDw:
     def __init__(self, fs, num_target, num_chans, num_filter, num_harms, stimTime, parameter, width, winNum,method):
-        print('******************************************')
-        print('parameter list')
-        print('target:', num_target)
-        print('number of filter bank:', num_filter)
-        print('parameter:', parameter)
-        print('width:', width)
+        algo_log('******************************************', level="debug")
+        algo_log('parameter list',level="debug")
+        algo_log(f"target: {num_target}", level="debug")
+        algo_log(f"number of filter bank: {num_filter}", level="debug")
+        algo_log(f"parameter: {parameter}", level="debug")
+        algo_log(f"width: {width}", level="debug")
         self.phase = 0
         self.bandWidth = width
         self.winNum = winNum
@@ -237,7 +238,7 @@ class FbccaDw:
                 dataFiltered, self.notchZh[0] = lfilter(self.north_b, self.north_a, data, zi=self.notchZh[0])
             return np.asmatrix(dataFiltered)
         except Exception:
-            print(Exception)
+            algo_log(f"Exception: {Exception}", level="debug")
 
     '''
     getDataQ

Tools/beta_calculate.py

@@ -0,0 +1,73 @@
+import numpy as np
+from scipy.signal import welch
+from collections import deque
+
+
+
+class Beta_Calculate():
+    def __init__(self, Threshold_value_low, Threshold_value_high, fs=250, win_len=5, config=None):
+        self.Threshold_value_low = Threshold_value_low
+        self.Threshold_value_high = Threshold_value_high
+        self.fs = fs
+        self.beta_result = []
+        self.eegQueue = deque(maxlen=win_len)
+
+    def calculate_all(self, data, fs, nperseg=1000):
+        mean_x = np.mean(data, axis=-1, keepdims=True)
+        data = data - mean_x
+        freqs, psd = self.compute_psd_multichannel(data, fs, nperseg)
+        beta_psd = np.sum(self.band_psd(freqs, psd, (13, 30)))
+        alpha_psd = np.sum(self.band_psd(freqs, psd, (8, 13)))
+        theta_psd = np.sum(self.band_psd(freqs, psd, (4, 8)))
+        
+        # print(f"[功率] β={beta_psd:.2f} | α={alpha_psd:.2f} | θ={theta_psd:.2f}")
+
+        return beta_psd, alpha_psd, theta_psd
+
+    def compute_psd_multichannel(self, data, fs=250, nperseg=1000):
+        n_samples = data.shape[-1]
+        if n_samples < nperseg:
+            nperseg = n_samples
+        
+        noverlap = 500
+        if noverlap >= nperseg:
+            noverlap = int(nperseg / 2)
+
+        if nperseg == 0:
+            return np.array([]), np.zeros((data.shape[0], 0))
+
+        freqs, psd = welch(data, fs=fs, nperseg=nperseg, noverlap=noverlap, axis=-1)
+        return freqs, psd
+
+    def band_psd(self, freqs, psd, band):
+        idx = np.logical_and(freqs >= band[0], freqs <= band[1])
+        return np.sum(psd[:, idx], axis=-1)
+
+
+    def reset_queue(self):
+        self.eegQueue.clear()
+
+
+    def queueOpt(self, data):
+        if data is None or data.size == 0:
+            return None
+        if len(self.eegQueue) < self.eegQueue.maxlen:
+            self.eegQueue.append(data)
+        else:
+            self.eegQueue.append(data)
+
+        if len(self.eegQueue) == self.eegQueue.maxlen:
+            eegData = np.hstack([self.eegQueue[i] for i in range(len(self.eegQueue))])
+            if eegData.size == 0:
+                return None
+            eegData -= np.mean(eegData, axis=-1, keepdims=True)
+
+            beta_psd, alpha_psd, theta_psd = self.calculate_all(eegData, fs=self.fs, nperseg=1000)
+
+            return (beta_psd)
+
+
+
+
+
+

ZeroMQClient_mock.py

@@ -0,0 +1,167 @@
+import zmq
+import time
+import json
+import os
+import threading
+
+def receive_messages(socket, stop_event):
+    """
+    后台线程函数，用于持续接收服务器消息
+    
+    Args:
+        socket (zmq.Socket): ZeroMQ套接字
+        stop_event (threading.Event): 停止事件，用于通知线程退出
+    """
+    print("开始持续接收服务器数据...")
+    print("-" * 50)
+    
+    while not stop_event.is_set():
+        try:
+            # 设置接收超时为1秒，避免阻塞
+            socket.setsockopt(zmq.RCVTIMEO, 1000)
+            # 接收服务器的消息
+            frames = socket.recv_multipart()
+            
+            # DEALER 套接字接收消息格式：[身份标识, 空帧, 消息内容]
+            # 使用frames[-1]获取最后一帧，无论中间有多少空帧
+            if len(frames) >= 2:
+                message = frames[-1].decode('utf-8')
+                
+                # 尝试解析为JSON格式
+                try:
+                    json_message = json.loads(message)
+                    # 检查消息长度
+                    json_str = str(json_message)
+                    if len(json_str) > 100:
+                        print(f"收到服务器数据 (JSON): {json_str[:100]}...")
+                    else:
+                        print(f"收到服务器数据 (JSON): {json_message}")
+                except json.JSONDecodeError:
+                    # 检查消息长度
+                    if len(message) > 100:
+                        print(f"收到服务器数据 (原始): {message[:100]}...")
+                    else:
+                        print(f"收到服务器数据 (原始): {message}")
+            else:
+                print(f"收到服务器数据 (格式异常): {frames}")
+                
+        except zmq.Again:
+            # 接收超时，继续循环
+            continue
+        except Exception as e:
+            print(f"接收消息时发生错误: {e}")
+            # 短暂暂停后继续接收
+            time.sleep(1)
+    
+    print("接收线程已停止。")
+
+def zero_mq_client(server_address="tcp://127.0.0.1:8099"):
+    """
+    ZeroMQ客户端函数，用于与服务器通信
+    
+    Args:
+        server_address (str): 服务器地址，格式为"tcp://IP:端口"
+    """
+    # 创建 ZeroMQ 上下文
+    context = zmq.Context()
+
+    # 创建 DEALER 套接字
+    socket = context.socket(zmq.DEALER)
+
+    # 生成唯一的身份标识
+    identity = str('wdd').encode('utf-8')
+    socket.setsockopt(zmq.IDENTITY, identity)
+
+    try:
+        # 连接到服务器
+        print(f"连接到服务器 {server_address}...")
+        socket.connect(server_address)
+        
+        # 定义消息集
+        message_set = [
+            {"method": "sync", "params": 1},
+            {"method": "decoderClass", "params": "mi"},
+            {"method": "decoderClass", "params": "ssvep"},
+            {"method": "decoderClass", "params": "ssmvep"},
+            {"method": "decoderClass", "params": "blink"},
+            {"method": "decoderClass", "params": "concentration"},
+            {"method": "train", "params": 0},
+            {"method": "train", "params": 1},
+            {"method": "rest", "params": 0},
+            {"method": "predict", "params": 1},
+            {"method": "getReport", "params": 0},
+            {"method": "targetFreqs", "params": [11, 12, 13]}
+        ]
+
+        # 打印消息集
+        print("消息集：")
+        for i, msg in enumerate(message_set):
+            print(f"[{i}] {msg}")
+        print("-" * 50)
+        
+        # 创建停止事件
+        stop_event = threading.Event()
+        
+        # 启动接收线程
+        receive_thread = threading.Thread(target=receive_messages, args=(socket, stop_event))
+        receive_thread.daemon = True  # 设置为守护线程，主线程退出时自动退出
+        receive_thread.start()
+        
+        # 主线程处理控制台输入
+        print("输入消息序号发送对应消息，输入'q'退出程序：")
+        while True:
+            try:
+                # 获取用户输入
+                user_input = input("请输入消息序号: ")
+                
+                # 检查是否退出
+                if user_input.lower() == 'q':
+                    print("正在退出程序...")
+                    break
+                
+                # 尝试转换为整数
+                msg_index = int(user_input)
+                
+                # 检查序号是否有效
+                if 0 <= msg_index < len(message_set):
+                    # 获取对应的消息
+                    selected_message = message_set[msg_index]
+                    
+                    # 将消息转换为 JSON 字符串
+                    json_message = json.dumps(selected_message)
+                    
+                    # 打印发送信息
+                    print(f"\n发送消息 (大小: {len(json_message)} 字节)...")
+                    print(f"消息方法: {selected_message['method']}")
+                    print(f"参数值: {selected_message['params']}")
+                    
+                    # DEALER 套接字发送消息，包含身份标识和空帧
+                    socket.send_multipart([identity, json_message.encode('utf-8')])
+                    print("消息发送完成！")
+                    print("-" * 50)
+                else:
+                    print(f"无效的消息序号，请输入 0-{len(message_set)-1} 之间的数字。")
+                    print("消息集：")
+                    for i, msg in enumerate(message_set):
+                        print(f"[{i}] {msg}")
+                    print("-" * 50)
+                    
+            except ValueError:
+                print("请输入有效的数字或'q'退出。")
+            except Exception as e:
+                print(f"处理输入时发生错误: {e}")
+
+    except KeyboardInterrupt:
+        print("\n程序被手动终止。")
+    finally:
+        # 停止接收线程
+        stop_event.set()
+        # 等待接收线程停止
+        time.sleep(1)
+        # 关闭套接字和上下文
+        socket.close()
+        context.term()
+        print("客户端已关闭。")
+
+if __name__ == "__main__":
+    zero_mq_client()

Zmq/dataBuffer.py

@@ -5,12 +5,13 @@
 import numpy as np
 from scipy import signal
 import threading
+from logs.log import algo_log
 
 class ParadigmRingBuffer:
     def __init__(self, n_chan, n_points):
         self.n_chan = n_chan
         self.n_points = n_points
-        self.buffer = np.zeros((n_chan, n_points))
+        self.buffer = np.zeros((n_chan, n_points), dtype=np.float64)
         self.currentPtr = 0
         self.readPtr = 0
         self.nUpdate = 0
@@ -19,7 +20,8 @@ class ParadigmRingBuffer:
     ## append buffer and update current pointer
     def appendBuffer(self, data):
         if self.nUpdate == self.n_points:
-            raise Exception("Buffer is full")
+        #     raise Exception("Buffer is full")
+            algo_log("ParadigmRingBuffer is full", record_once=True)
 
         n = data.shape[1]
 
@@ -65,13 +67,56 @@ class ParadigmRingBuffer:
         '''
         return self.nUpdate    
     
+        # ========== 各范式数据访问接口 ==========
+    def get_MIData(self):
+        """获取MI导联数据 (21通道 + 事件)"""
+        data = self.getData(self.GetDataLenCount())
+        rows_to_extract = [8, 15, 12, 14, 18, 23, 16, 59, 50, 58, 17, 45, 29, 11, 10, 19, 20, 61, 51, 60, 21, 64, 65]
+        row_to_select = np.array(rows_to_extract)
+        if data.shape[1] > 0:
+            return data[row_to_select, :]
+        return np.zeros((len(rows_to_extract), 0))
+
+    def get_SSMVEPData(self):
+        """获取SSMVEP导联数据 (8通道 + 事件)"""
+        data = self.getData(self.GetDataLenCount())
+        rows_to_extract = [13, 3, 2, 46, 9, 54, 47, 55, 64, 65]
+        row_to_select = np.array(rows_to_extract)
+        if data.shape[1] > 0:
+            return data[row_to_select, :]
+        return np.zeros((len(rows_to_extract), 0))
+
+    def getDataViaSSVEP(self, count):
+        """获取SSVEP数据 (8通道 + 事件)"""
+        data = self.getData(count)
+        rows_to_extract = [13, 3, 2, 46, 9, 54, 47, 55, 64]
+        row_to_select = np.array(rows_to_extract)
+        if data.shape[1] > 0:
+            return data[row_to_select, :]
+        return np.zeros((len(rows_to_extract), 0))
+
+    def get_concentrateData(self, count):
+        """获取专注力数据 (2通道)"""
+        data = self.getData(count)
+        rows_to_extract = [0, 1]
+        row_to_select = np.array(rows_to_extract)
+        if data.shape[1] > 0:
+            return data[row_to_select, :]
+        return np.zeros((len(rows_to_extract), 0))
+
+    def get_blinkData(self, count):
+        """获取眨眼数据 (2通道)"""
+        data = self.getData(count)
+        rows_to_extract = [0, 1]
+        row_to_select = np.array(rows_to_extract)
+        if data.shape[1] > 0:
+            return data[row_to_select, :]
+        return np.zeros((len(rows_to_extract), 0))
 
     # reset buffer
     def resetAllPara(self):
         self.nUpdate = 0
         self.currentPtr = 0
-        self.readPtr = 0  # add by lizhenhua 清空读指针
-        self.buffer = np.zeros((self.n_chan, self.n_points))  # add by lizhenhua 清空环形缓冲区
-        
-
+        self.readPtr = 0
+        self.buffer.fill(0.0)
 

Zmq/filterProcess.py

@@ -3,206 +3,310 @@
 数据滤波模块
 """
 import numpy as np
+import time
 import threading
+import queue
+from scipy import signal
 from logs.log import algo_log
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from Tools.beta_calculate import Beta_Calculate
 
 class FilterRingBuffer:
     def __init__(self, n_chan, n_points):
-        """
-        初始化纯数据环形缓存
-        :param n_chan: 通道数
-        :param n_points: 总缓存点数（与paradigmRingBuffer参数完全一致）
-        """
         self.n_chan = n_chan
         self.n_points = n_points
-
         self.buffer = np.zeros((n_chan, n_points), dtype=np.float64)
-        self.current_ptr = 0  # 写入指针
-        self.total_samples = 0  # 已写入总点数
-        
-        # 线程安全锁（多线程环境必须）
-        self.lock = threading.Lock()
+        self.current_ptr = 0
+        self.total_samples = 0
+        self.lock = threading.Lock()  # 仅保护元数据
+        self.has_new_data = False
 
     def appendBuffer(self, data):
-        """
-        追加数据到缓存（与paradigmRingBuffer接口一致）
-        :param data: 输入数据，shape=(n_chan, n_samples)
-        """
-        with self.lock:
         n = data.shape[1]
         if n == 0:
             return
         
-            # 环形写入逻辑
-            write_end = self.current_ptr + n
+        # 仅加锁读取/更新元数据
+        with self.lock:
+            old_ptr = self.current_ptr
+            new_ptr = (old_ptr + n) % self.n_points
+            new_total = min(self.total_samples + n, self.n_points)
+            self.has_new_data = True
+        
+        # 数组写入（耗时操作，移出锁外）
+        write_end = old_ptr + n
         if write_end <= self.n_points:
-                self.buffer[:, self.current_ptr:write_end] = data
+            self.buffer[:, old_ptr:write_end] = data
         else:
-                split = self.n_points - self.current_ptr
-                self.buffer[:, self.current_ptr:] = data[:, :split]
+            split = self.n_points - old_ptr
+            self.buffer[:, old_ptr:] = data[:, :split]
             self.buffer[:, :write_end - self.n_points] = data[:, split:]
         
-            # 更新指针和计数
-            self.current_ptr = write_end % self.n_points
-            self.total_samples = min(self.total_samples + n, self.n_points)
+        # 再次加锁更新最终元数据
+        with self.lock:
+            self.current_ptr = new_ptr
+            self.total_samples = new_total
+
+    # ========== 新增：获取&清空新数据标记的方法 ==========
+    def check_and_clear_new_data(self):
+        """检查是否有新数据，并一次性清空标记（消费后重置）"""
+        with self.lock:
+            flag = self.has_new_data
+            if flag:
+                self.has_new_data = False
+            return flag
 
     def getData(self, count):
-        """
-        从读指针位置读取count个点（与paradigmRingBuffer接口一致）
-        :param count: 读取点数
-        :return: np.ndarray, shape=(n_chan, count)
-        """
+        # 加锁获取最新元数据
         with self.lock:
             count = min(count, self.total_samples)
             if count == 0:
                 return np.zeros((self.n_chan, 0))
-            
-            # 环形读取逻辑（与paradigmRingBuffer完全相同）
             end = self.current_ptr
             start = end - count
+        
+        # 数据读取、切片、拼接（无锁）
         if start >= 0:
-                return self.buffer[:, start:end].copy()
+            res = self.buffer[:, start:end].copy()
         else:
             part1 = self.buffer[:, start:]
             part2 = self.buffer[:, :end]
-                return np.concatenate((part1, part2), axis=1)
+            res = np.concatenate((part1, part2), axis=1).copy()
+        return res
 
     def get_latest_n_points(self, n):
-        """
-        扩展方法：获取最新的n个点（不移动读指针，用于滑动窗口）
-        :param n: 点数
-        :return: np.ndarray, shape=(n_chan, n)
-        """
         with self.lock:
             if self.total_samples < n:
                 return None
         return self.getData(n)
 
     def GetDataLenCount(self):
-        """获取当前缓存总点数（兼容原有接口）"""
         with self.lock:
             return self.total_samples
 
     def resetAllPara(self):
-        """重置所有缓存和指针（兼容原有接口）"""
         with self.lock:
             self.buffer.fill(0.0)
             self.current_ptr = 0
             self.total_samples = 0
+            self.has_new_data = False  # 重置时清空新数据标记
 
 # -----------------------------------------------------------------------------
-# 2. 独立滑动滤波类（仅负责滤波业务逻辑，不关心缓存实现）
-# 可替换任意缓存实现，只要实现appendBuffer、get_latest_n_points接口
+# 2. 独立 Beta PSD 计算线程（避免阻塞滤波主循环的 200ms 定时）
 # -----------------------------------------------------------------------------
-class SlidingFilter:
+class BetaPsdCalculator(threading.Thread):
+    """独立的 Beta PSD 计算线程，使用队列与滤波主线程解耦"""
+
+    def __init__(self, fs=250, window_size=750):
+        super().__init__(daemon=True)
+        self.fs = fs
+        self.window_size = window_size
+        self._beta_calc = Beta_Calculate(Threshold_value_low=0, Threshold_value_high=0, fs=fs)
+        self._input_queue = queue.Queue(maxsize=2)
+        self._running = threading.Event()
+        self._running.set()
+        self._latest_beta = None
+        self._beta_lock = threading.Lock()
+        self.beta_broadcast_callback = None
+
+    def push_data(self, data):
+        """供外部调用的线程安全数据推送接口"""
+        try:
+            self._input_queue.put_nowait(data)
+        except queue.Full:
+            try:
+                self._input_queue.get_nowait()
+            except queue.Empty:
+                pass
+            try:
+                self._input_queue.put_nowait(data)
+            except queue.Full:
+                pass
+
+    def get_latest_beta(self):
+        """获取最新的 beta 值（线程安全）"""
+        with self._beta_lock:
+            return self._latest_beta
+
+    def run(self):
+        while self._running.is_set():
+            try:
+                data = self._input_queue.get(timeout=1.5)
+                if data is None:
+                    break
+                try:
+                    beta_psd, _, _ = self._beta_calc.calculate_all(
+                        data, fs=self.fs, nperseg=min(self.window_size, data.shape[1])
+                    )
+                    with self._beta_lock:
+                        self._latest_beta = round(float(beta_psd), 3)
+                    if self.beta_broadcast_callback is not None:
+                        self.beta_broadcast_callback(self._latest_beta)
+                except Exception as e:
+                    algo_log(f"Beta PSD 计算异常: {e}", level='error')
+            except queue.Empty:
+                pass
+
+    def stop(self):
+        """停止计算线程"""
+        self._running.clear()
+        try:
+            self._input_queue.put_nowait(None)
+        except queue.Full:
+            pass
+        if self.is_alive():
+            self.join(timeout=2)
+
+
+# -----------------------------------------------------------------------------
+# 3. 独立滑动滤波类（仅负责滤波业务逻辑，不关心缓存实现）
+# -----------------------------------------------------------------------------
+class SlidingFilter(threading.Thread):
     def __init__(
         self,
+        ring_buffer: FilterRingBuffer,
         n_chan=66,
         srate=250,
-        buffer_sec=5,
         window_sec=3,
-        step_sec=0.2,
-        packet_size=5
+        step_sec=0.2
     ):
-        """
-        初始化滑动滤波器
-        :param n_chan: 通道数
-        :param srate: 采样率
-        :param buffer_sec: 总缓存时长（秒）
-        :param window_sec: 滤波窗口时长（秒）
-        :param step_sec: 滑动步长/输出时长（秒）
-        :param packet_size: 每包数据点数（20ms一包=5点）
-        """
+        super().__init__(daemon=True)
         # 核心参数
         self.n_chan = n_chan
         self.srate = srate
-        self.buffer_size = int(srate * buffer_sec)
-        self.window_size = int(srate * window_sec)
-        self.step_size = int(srate * step_sec)
-        self.packet_size = packet_size
+        self.step_sec = step_sec  # 200ms滑动步长
+        self.window_sec = window_sec  # 3秒窗口
+        self.step_sec = step_sec  # 200ms滑动步长
+        self.window_size = int(srate * window_sec)  # 3秒点数：250*3=750
+        self.step_size = int(srate * step_sec)      # 200ms点数：250*0.2=50
 
-        # 初始化纯数据缓存（解耦核心）
-        self.buffer = FilterRingBuffer(n_chan, self.buffer_size)
+        # 关联ZMQServer的环形缓存（解耦：仅依赖接口）
+        self.ring_buffer = ring_buffer
+        # 线程控制
+        self.running = threading.Event()
+        self.running.set()
+        # 滤波结果回调（外部可注册，获取滤波后的数据）
+        self.filter_result_callback = None
 
-        # 滤波触发计数器
-        self.packet_count = 0
-        self.ready_to_filter = False
+        # beta 每秒触发计数（200ms步长，5次 = 1s）
+        self._beta_step_counter = 0
+        self._beta_steps_per_second = max(1, int(round(1.0 / step_sec)))  # 5
 
-        # 预计算滤波器系数
+        self.slide_window = None  # 滑动窗口缓存 (n_chan, window_size)
+        self.slide_ready = False  # 窗口是否已填满初始数据
+        # 预计算滤波器系数（仅执行一次）
         self._init_filters()
 
+        # 独立的 Beta 计算线程（避免阻塞滤波主循环）
+        self._beta_thread = BetaPsdCalculator(fs=srate, window_size=self.window_size)
+
+    def start(self):
+        """同时启动 Beta 计算线程和滤波主线程"""
+        self._beta_thread.start()
+        super().start()
+
+    def set_beta_broadcast_callback(self, callback):
+        """注册 Beta PSD 广播回调函数"""
+        self._beta_thread.beta_broadcast_callback = callback
+
     def _init_filters(self):
         """预计算所有滤波器系数（仅执行一次）"""
         # 50Hz工频陷波（Q=30，工业标准）
         self.b_notch, self.a_notch = signal.iirnotch(50, 30, self.srate)
-        # 8~30Hz带通FIR（65阶，线性相位）
+        # 0.5~45Hz带通FIR（65阶，线性相位）
         self.b_bp = signal.firwin(
             numtaps=65,
-            cutoff=[8/(self.srate/2), 30/(self.srate/2)],
+            cutoff=[0.5/(self.srate/2), 45/(self.srate/2)],
             pass_zero=False,
             window='hamming'
         )
         self.a_bp = np.array([1.0])
 
-    def append_and_check_trigger(self, raw_data):
-        """
-        追加单包原始数据并检查是否触发滤波
-        :param raw_data: 上位机原始数据，shape=(packet_size, n_chan)
-        :return: bool: 是否触发本次滤波
-        """
-        # 转置为标准格式：(通道数, 点数)
-        data = raw_data.T.astype(np.float64)
-        
-        # 写入缓存（纯缓存操作）
-        self.buffer.appendBuffer(data)
-        
-        # 更新包计数器
-        self.packet_count += 1
-        
-        # 检查滤波触发条件：数据≥窗口长度 且 累计满一个步长的包数
-        packets_per_step = int(self.step_size / self.packet_size)  # 10包=200ms
-        if (self.buffer.GetDataLenCount() >= self.window_size 
-            and self.packet_count >= packets_per_step):
-            self.packet_count = 0
-            self.ready_to_filter = True
-            return True
-        return False
-
-    def filter_and_get_output(self):
-        """
-        执行滤波并返回无边界效应的输出数据
-        :return: np.ndarray: 滤波后数据，shape=(n_chan, step_size)
-        """
-        if not self.ready_to_filter:
-            return None
-        
-        # 获取最新的完整滤波窗口数据
-        window_data = self.buffer.get_latest_n_points(self.window_size)
-        if window_data is None:
-            self.ready_to_filter = False
-            return None
-        
+    def _filter_window_data(self, window_data):
+        """对3秒窗口数据执行滤波，返回 (无边界效应的200ms数据, 完整3s滤波数据)"""
         # 零相位滤波（无延迟，无边界效应）
         filtered = window_data - np.mean(window_data, axis=-1, keepdims=True)
         filtered = signal.filtfilt(self.b_notch, self.a_notch, filtered, axis=-1)
         filtered = signal.filtfilt(self.b_bp, self.a_bp, filtered, axis=-1)
 
-        # 提取倒数第二个步长的数据（完全避开两端边界效应）
+        # 提取倒数第二个200ms的数据（完全避开两端边界效应）
+        # 窗口长度750，步长50 → start=750-100=650，end=750-50=700
         start_idx = self.window_size - 2 * self.step_size
         end_idx = self.window_size - self.step_size
         output_data = filtered[:, start_idx:end_idx].copy()
+        return output_data, filtered
 
-        # 重置触发标志
-        self.ready_to_filter = False
+    def run(self):
+        """线程主逻辑：精确200ms触发一次滤波"""
+        interval = self.step_sec  # 0.2s
+        # 以启动时刻为绝对时间基准（核心改动）
+        base_time = time.perf_counter()
+        frame_count = 0  # 帧计数器，用于对齐时序
 
-        return output_data
+        while self.running.is_set():
+            # 计算理论执行时刻：严格按帧序号 × 步长
+            expect_time = base_time + frame_count * interval
+            current_time = time.perf_counter()
 
-    def reset(self):
-        """重置滤波器和缓存"""
-        self.buffer.resetAllPara()
-        self.packet_count = 0
-        self.ready_to_filter = False
+            # 精确定时等待
+            if current_time < expect_time:
+                time.sleep(expect_time - current_time)
+            else:
+                # 处理超时：仅告警，不重置基准（防止累积偏移）
+                algo_log(f"滤波任务超时，偏移 {(current_time - expect_time)*1000:.1f} ms", level='debug')
             
-    def get_buffer_length(self):
-        """获取当前缓存数据长度"""
-        return self.buffer.GetDataLenCount()
+            frame_count += 1  # 帧序号自增，保证周期绝对稳定
+            if not self.ring_buffer.check_and_clear_new_data():
+                # 无新数据，不执行滤波、不发送数据
+                continue
+            
+            # ========== 原有滤波逻辑 ==========
+            try:
+                if not self.slide_ready:
+                    # 阶段1：首次填满3s初始窗口
+                    full_data = self.ring_buffer.get_latest_n_points(self.window_size)
+                    if full_data is None:
+                        algo_log("初始窗口数据不足", level='debug')
+                        continue
+                    self.slide_window = full_data
+                    self.slide_ready = True
+                else:
+                    # 阶段2：正常滑动 → 取最新50个新点，增量拼接
+                    new_step_data = self.ring_buffer.get_latest_n_points(self.step_size)
+                    if new_step_data is None:
+                        algo_log("滑动步长数据不足", level='debug')
+                        continue
+                    # 增量滑动：丢弃前50点，拼接新50点（标准滑动窗口）
+                    self.slide_window = np.hstack([
+                        self.slide_window[:, self.step_size:],
+                        new_step_data
+                    ])
+                
+                filtered_data, filtered_full = self._filter_window_data(self.slide_window[:64, :])
+
+                # Beta PSD 每秒计算一次
+                self._beta_step_counter += 1
+                if self._beta_step_counter >= self._beta_steps_per_second:
+                    self._beta_step_counter = 0
+                    self._beta_thread.push_data(filtered_full[:2, :])
+
+                if self.filter_result_callback is not None:
+                    self.filter_result_callback(filtered_data)
+            except Exception as e:
+                algo_log(f"滤波执行异常: {e}", level='error')
+
+    def set_result_callback(self, callback):
+        """注册滤波结果回调函数"""
+        self.filter_result_callback = callback
+
+    def stop(self):
+        """停止滤波线程和 Beta 计算线程"""
+        self._beta_thread.stop()
+        self.running.clear()
+        if self.is_alive():
+            self.join(timeout=1)
+            if self.is_alive():
+                algo_log("警告：滤波线程在1秒内未正常退出，可能存在阻塞操作", level="WARNING")
+        algo_log("滤波线程已停止")

Zmq/zmqServer.py

@@ -1,241 +1,435 @@
+# -*-coding:utf-8 -*-
+import ast
 import numpy as np
-import zmq
 import threading
+import zmq
 import json
 import queue
-# from Device.SunnyLinker import SunnyLinker64
-from dataBuffer import ParadigmRingBuffer
-from filterProcess import FilterRingBuffer
+from typing import Dict
+import datetime
+import time
+
+from Zmq.dataBuffer import ParadigmRingBuffer
+from Zmq.filterProcess import FilterRingBuffer
+from PubLibrary.InifileHelper import IniRead
 from logs.log import algo_log
 
+zmqServer_host = str(IniRead('system', 'zmqServer_host', '127.0.0.1'))
+
 class zmqServer(threading.Thread):
     def __init__(self, host='0.0.0.0', cmd_port=8099, data_port=8100, device_info=None):
         threading.Thread.__init__(self)
-        self.host = host
-        self.cmd_port = cmd_port    # 命令交互端口
-        self.data_port = data_port  # 数据接收端口
+        self.device_info = device_info
+        
+        self.host = zmqServer_host
+        
+        self.cmd_port = cmd_port    # 命令交互端口：收JSON命令 + 返JSON结果
+        self.data_port = data_port  # 数据交互端口：收二进制原始脑电 + 返二进制滤波结果
         self.running = False
         
         # 原有业务状态变量
-        # self.get_Impedance = False  # 是否返回阻抗值
-        # self.open_Impedance = None  # 是否开启阻抗检测功能
-        self.StartDecode = False    # false 停止解码，true=开始解码
-        self.StartTrain = False     # False未进入训练状态，True处于训练状态
-        self.state_mode = None      # 'train'为训练状态，’rest'为休息状态,'test'为测试状态
-        self.currentLabel = -1      # 接收刺激端消息，了解刺激端当前的训练标签
-        self.IsExitApp = False      # 当socket收到2的时候，就置为True，代表要退出系统了。
-        # self.getReport = False      # 获取训练报告内容
+        self.open_Impedance = False   #当前系统处于阻抗检测状态
+        self.StartDecode = False
+        self.StartTrain = False
+        self.state_mode = None
+        self.currentLabel = -1
+        self.IsExitApp = False
         self.daemon = True
         
-        # 范式数据缓存
-        self.paradigmBuffer = ParadigmRingBuffer(66, 2500)
-        self.filterBuffer = FilterRingBuffer(66, 2500)
+        # 双环形缓冲区
+        self.paradigmBuffer = ParadigmRingBuffer(
+            self.device_info['channel_nums'], 
+            self.device_info['sample_rate'] * 10
+        )
+        self.filterBuffer = FilterRingBuffer(
+            self.device_info['channel_nums'], 
+            self.device_info['sample_rate'] * 10
+        )
+        self.paradigmBufferLock = threading.Lock()
+        self.filterBufferLock = threading.Lock()
         
-        
-        # 命令与数据通信
+        # ZMQ上下文与套接字
         self.context = zmq.Context()
-        # 指令通道 (8099) - ROUTER：短JSON命令，低频率
+        
+        # 8099命令端口：ROUTER
         self.cmd_socket = self.context.socket(zmq.ROUTER)
-        self.cmd_socket.setsockopt(zmq.RCVHWM, 100)   # 指令不需要大缓存，100条足够
-        self.cmd_socket.setsockopt(zmq.SNDHWM, 100)
-        self.cmd_socket.setsockopt(zmq.TCP_NODELAY, 1) # 禁用Nagle算法，降低指令延迟
+        self.cmd_socket.setsockopt(zmq.SocketOption.RCVHWM, 100)
+        self.cmd_socket.setsockopt(zmq.SocketOption.SNDHWM, 100)
         self.cmd_socket.bind(f"tcp://{self.host}:{cmd_port}")
 
-        # 数据通道 (8100) - ROUTER：高频脑电二进制流
+        # 8100数据端口：ROUTER
         self.data_socket = self.context.socket(zmq.ROUTER)
-        self.data_socket.setsockopt(zmq.RCVHWM, 500)  # 500包=10秒缓存，足够应对短时卡顿
-        self.data_socket.setsockopt(zmq.TCP_NODELAY, 1) # 禁用Nagle算法，减少数据传输延迟
+        self.data_socket.setsockopt(zmq.SocketOption.RCVHWM, 500)
+        self.data_socket.setsockopt(zmq.SocketOption.SNDHWM, 100)  # 添加发送高水位线
         self.data_socket.bind(f"tcp://{self.host}:{data_port}")
 
-        # Poller 轮训器（保持不变）
+        # Poller轮询器
         self.poller = zmq.Poller()
         self.poller.register(self.cmd_socket, zmq.POLLIN)
         self.poller.register(self.data_socket, zmq.POLLIN)
                 
         # 业务变量
         self.targetFreqs = []
-        self.changeTarget = False  # 更换目标频率
-        # self.sunnyLinker = SunnyLinker64(None, None, None, None,None) #单例模式类，已在Decoder实例化
+        self.changeTarget = False
         self.labels = [0x01, 0x02, 0x03]
-        self.decoder_switch = False #更换解码器
-        self.decoder_class = None #解码器类别 'ssvep','ssmvep','mi'
+        self.decoder_switch = False
+        self.decoder_class = None
         
-        # 客户端管理 - 区分命令/数据客户端
-        self.cmd_clients = set()    # 命令端口客户端ID
-        self.data_clients = set()   # 数据端口客户端ID
-        self.send_queue = queue.Queue()  # 发送队列（仅用于命令端口广播）
+        # 客户端管理（单客户端场景）
+        self.cmd_clients = set()
+        self.data_clients = set()
+        self.current_data_client = None  # 唯一数据客户端身份，用于发送滤波结果
         
+        # 发送队列（双端口分离）
+        self.cmd_send_queue = queue.Queue()  # 8099端口命令结果队列
+        self.data_send_queue = queue.Queue() # 8100端口滤波数据队列
+        
+        # 范式buffer与事件检测参数
+        self.predict_event = 99
+        self.events = [1, 2, self.predict_event]
+        self.latency = 50
+        self.train_latency = 50
+        self.count_events = {}
+        self.epoch_finished = False
+        self.pack_contain_event = False
+        self.event_inner_idx = -1
+        self.interval_inited = False
+        self.last_epoch_finish_time = None
+
+    def reset_state(self):
+        """清空采集器状态和缓存数据"""
+        with self.paradigmBufferLock:
+            self.paradigmBuffer.resetAllPara()
+        self.count_events = {}
+        self.epoch_finished = False
+        self.pack_contain_event = False
+        self.event_inner_idx = -1
+        self.interval_inited = False
+
+    def interval_init(self, decoder_class):
+        if decoder_class == 'ssmvep':
+            interval_epoch = ast.literal_eval(IniRead('system', 'SSMVEP_IntervalEpoch'))  # [0.2, 2.2]
+            self.interval_epoch = [int(i * self.device_info['sample_rate']) for i in interval_epoch]  # [50, 550]
+            self.train_epoch = [
+                int(self.interval_epoch[0]),
+                int(self.interval_epoch[1] + 0.1 * self.device_info['sample_rate'])
+            ] # [50, 575]
+            self.latency = (self.interval_epoch[1] + 0.1 * self.device_info['sample_rate']) // 5       #115包, 575个点
+            self.train_latency = (self.train_epoch[1] + 0.1 * self.device_info['sample_rate']) // 5    #120包  600个点
+
+        elif decoder_class == 'mi':
+            interval_epoch = ast.literal_eval(IniRead('system', 'MI_IntervalEpoch'))  # [0.5, 4.5]
+            self.interval_epoch = [int(i * self.device_info['sample_rate']) for i in interval_epoch]  #[125, 1125]
+            self.train_epoch = self.interval_epoch.copy()
+            self.latency = self.interval_epoch[1] // 5  #225
+            self.train_latency = self.latency    #225
+
+        algo_log(f"时间窗初始化完成: {interval_epoch}", level="INFO")
+        self.count_events: Dict[str, int] = {}
+        self.event_inner_idx = -1
+        self.epoch_finished = False
+        self.pack_contain_event = False
+        self.predict_event = 99
+        self.events = [1, 2, self.predict_event]
+        self.interval_inited = True
+
+    # -------------------------- 8099端口：命令结果广播 --------------------------
     def broadcast_message(self, method, params):
-        """Put message into queue to be sent to all command clients"""
-        self.send_queue.put((method, params))
+        """
+        向所有8099端口客户端广播JSON格式的命令结果
+        用于：解码结果、训练状态、错误提示、进度通知等
+        """
+        self.cmd_send_queue.put((method, params))
 
-    def _handle_cmd_message(self, frames):
-        """处理命令端口消息（原有命令交互逻辑）"""
-        if len(frames) < 3:
+    def _process_cmd_send_queue(self):
+        """处理8099端口发送队列，在主线程执行（保证ZMQ线程安全）"""
+        while not self.cmd_send_queue.empty():
+            method, params = self.cmd_send_queue.get()
+            if not self.cmd_clients:
+                continue
+                
+            try:
+                msg = {'method': method, 'params': params}
+                msg_bytes = json.dumps(msg).encode('utf-8')
+
+                if msg['method'] == 'beta_psd':
+                    algo_log(f"发送命令结果: {msg}", level="DEBUG", record_once=True)
+                else:
+                    algo_log(f"发送命令结果: {msg}", level="DEBUG")
+                
+                # 广播到所有命令客户端
+                for client_id in list(self.cmd_clients):
+                    try:
+                        self.cmd_socket.send_multipart([client_id, b"", msg_bytes])
+                    except Exception as e:
+                        algo_log(f"向命令客户端{client_id}发送失败: {e}", level="ERROR")
+                        self.cmd_clients.discard(client_id)
+                        
+            except Exception as e:
+                algo_log(f"命令结果打包失败: {e}", level="ERROR")
+
+    # -------------------------- 8100端口：滤波结果发送 --------------------------
+    def send_filtered_data(self, filtered_data):
+        """
+        向8100端口客户端发送二进制格式的滤波结果
+        用于：上位机实时绘图的脑电波形数据
+        :param filtered_data: 滤波后数据，shape=(通道数, 50)，float64格式
+        """
+        if self.current_data_client is None:
+            algo_log("数据客户端未连接，跳过滤波数据发送", level="WARNING")
             return
+            
+        # 转置为上位机需要的[50, 通道数]格式
+        filtered_data = filtered_data.T.astype(np.float64)
+        send_buf = filtered_data.tobytes()
+        # algo_log(f"发送滤波数据，长度: {len(send_buf)}字节, filtered_data.shape: {filtered_data.shape}", level="DEBUG", record_once=True)
+        self.data_send_queue.put(send_buf)
+
+    def _process_data_send_queue(self):
+        """处理8100端口发送队列，在主线程执行（保证ZMQ线程安全）"""
+        while not self.data_send_queue.empty():
+            send_buf = self.data_send_queue.get()
+            if self.current_data_client is None:
+                continue
+                
+            try:
+                # 标准ROUTER发送格式：[客户端ID, 空分隔帧, 数据帧]
+                self.data_socket.send_multipart([
+                    self.current_data_client, 
+                    b"", 
+                    send_buf
+                ])
+                algo_log(f"发送滤波数据成功，长度: {len(send_buf)}字节", level="DEBUG", record_once=True)
+                
+            except Exception as e:
+                algo_log(f"发送滤波数据失败: {e}", level="ERROR")
+                # 客户端断开，重置身份
+                self.current_data_client = None
+                self.data_clients.clear()
+
+    # -------------------------- 命令端口消息处理 --------------------------
+    def _handle_cmd_message(self, frames):
+        """处理8099端口JSON命令消息"""
+        if len(frames) < 3:
+            algo_log(f"无效命令帧：长度不足3帧，实际{len(frames)}", level="ERROR")
+            return
+        
         ident, _, message_bytes = frames[:3]
         
         # 注册新的命令客户端
         if ident not in self.cmd_clients:
             self.cmd_clients.add(ident)
-            print(f"New CMD Client Connected: {ident} (port: {self.cmd_port})")
+            algo_log(f"新命令客户端连接成功: {ident}", level="INFO")
         
-        # 解析消息
+        # 解析JSON命令
         try:
             message = json.loads(message_bytes.decode('utf-8'))
         except json.JSONDecodeError:
-            print(f"Invalid JSON from CMD client {ident}")
-            continue
-        print(f"Received CMD request: {message}")
+            algo_log(f"无效JSON命令: {message_bytes.hex()}", level="ERROR")
+            self.broadcast_message("error", {"code": 400, "message": "无效JSON格式"})
+            return
+        except Exception as e:
+            algo_log(f"_handle_cmd_message exception: {e}", level="ERROR")
+            return
             
+        algo_log(f"收到命令: {message}", level="INFO")
         method = message.get("method")
         params = message.get("params")
 
-        # 原有命令处理逻辑
+        # 命令处理逻辑
         if method == "sync":
             self.state_mode = 'sync'
-        if method == "targetFreqs":
+        elif method == "targetFreqs":
             if not isinstance(params, list):
-                print('targetFreqs must be a list')
-                continue
+                algo_log(f"targetFreqs must be a list")
+                return
             if params != self.targetFreqs:
                 self.targetFreqs = params
                 self.changeTarget = True
-        if method == "decoderClass":
+        elif method == "decoderClass":
             if not isinstance(params, str):
-                print('decoderClass must be a str')
-                continue
+                algo_log(f"decoderClass必须是字符串")
+                return
             if params != self.decoder_class:
                 self.decoder_class = params
                 self.decoder_switch = True
-        if method == "getReport":
-            self.getReport = True
-        if method == "train":#训练状态
+        elif method == "train":
             self.state_mode = 'train'
-            self.StartTrain = True
-            self.currentLabel = params  # 当前刺激端的训练标签
-            self.sunnyLinker.push_trigger(self.labels[self.currentLabel])
-        elif method == "predict":#预测状态
+            resp = {
+                "method": "train_response",
+                "params": {
+                    "code": 200,
+                    "message": "ok"
+                }
+            }
+            try:
+                resp_bytes = json.dumps(resp, ensure_ascii=False).encode("utf-8")
+                self.cmd_socket.send_multipart([ident, b"", resp_bytes])
+                algo_log(f"train 命令已即时回复客户端 {ident}", level="DEBUG")
+            except Exception as e:
+                algo_log(f"train 命令回复失败: {e}", level="ERROR")
+            return
+        elif method == "predict":
             self.state_mode = 'predict'
             if params == 1: #开始解码
                 self.StartDecode = True
-                self.sunnyLinker.push_trigger(0x63)
             elif params == 2: #停止解码
                 self.IsExitApp = True
                 self.running = False
-        elif method == "rest": #休息状态
+        elif method == "rest":
             self.state_mode = 'rest'
-        # elif method == "impedance":
-        #     if params == 1:
-        #         self.open_Impedance = True  # 开启阻抗
-        #         self.get_Impedance = True  # 返回阻抗
-        #     elif params == 2:
-        #         self.open_Impedance = False  # 关闭阻抗
-        #         self.get_Impedance = False  # 停止返回阻抗
+        elif method == "impedance":
+            if params == 1:
+                self.open_Impedance = True
+            elif params == 2:
+                self.open_Impedance = False
+        else:
+            self.broadcast_message("error", {"code": 404, "message": f"未知命令: {method}"})
 
+    # -------------------------- 数据端口消息处理 --------------------------
     def _handle_data_message(self, frames):
-        """
-        处理8100端口原始脑电二进制数据
-        固定格式：上位机发送 (5,66) float32 二维数组字节流（已转换为微伏物理量）→ 转置为 (66,5) 写入双缓冲区
-        """
-        # 1. 校验ZMQ消息帧完整性
-        if len(frames) < 3:
-            print(f"[ERROR] 无效数据帧：长度不足3帧，实际长度={len(frames)}")
+        """处理8100端口二进制脑电数据消息"""
+        algo_log(f"收到数据帧，总帧数：{len(frames)}", level="DEBUG", record_once=True)
+        # 然后再进行解析
+        if len(frames) == 4:
+            # 你的上位机格式
+            ident, sender_ident, empty_sep, data_bytes = frames[:4]
+        elif len(frames) == 3:
+            # 标准格式
+            ident, empty_sep, data_bytes = frames[:3]
+        elif len(frames) == 2:
+            ident, data_bytes = frames[:2]
+        else:
             return
-        
-        ident, _, data_bytes = frames[:3]
-
-        # 2. 客户端管理（单客户端场景，自动更新最新身份）
+        # 注册新的数据客户端（单客户端场景，自动覆盖旧身份）
         if ident not in self.data_clients:
+            self.data_clients.clear()  # 单客户端，只保留最新连接
             self.data_clients.add(ident)
-            self.current_data_client = ident  # 保存唯一客户端身份，用于后续回复滤波结果
-            print(f"[INFO] 新数据客户端连接成功：{ident}")
-
+            self.current_data_client = ident
+            algo_log(f"新数据客户端连接成功: {ident}", level="INFO")
         try:
-            # 3. 精确长度校验（核心：固定(5,66) float32 = 5*66*4=1320字节，与int32字节数相同）
-            EXPECTED_BYTES = 5 * 66 * 4  # 每个float32占4字节
+            # 精确长度校验
+            EXPECTED_BYTES = self.device_info['frame_points'] * self.device_info['channel_nums'] * np.dtype(np.float64).itemsize
             if len(data_bytes) != EXPECTED_BYTES:
-                print(f"[ERROR] 数据长度错误：期望{EXPECTED_BYTES}字节，实际{len(data_bytes)}字节")
+                algo_log(f"数据长度错误：期望{EXPECTED_BYTES}字节，实际{len(data_bytes)}字节", level="ERROR")
                 return
 
-            # 4. 零拷贝二进制解析 + 维度转换
-            # 步骤：字节流 → (330,) float32数组 → (5,66) 原始格式 → 转置为 (66,5) 缓冲区标准格式
-            data_np = np.frombuffer(data_bytes, dtype=np.float32)
-            # 重塑为上位机原始维度
-            data_np = data_np.reshape(5, 66)
-            # 转置为(通道数, 采样点数)标准格式，转换为float64保证滤波运算精度
+            # 零拷贝解析 + 维度转换
+            data_np = np.frombuffer(data_bytes, dtype=np.float64)
+            data_np = data_np.reshape(self.device_info['frame_points'], self.device_info['channel_nums'])
             data_np = data_np.T.astype(np.float64)
 
-            # 5. 同时写入双环形缓冲区（方法名与现有类保持一致：appendBuffer）
-            # 注意：上位机已发送微伏物理量，无需再乘以增益系数
-            self.paradigmBuffer.appendBuffer(data_np)
+            # 写入滤波缓冲区
+            with self.filterBufferLock:
                 self.filterBuffer.appendBuffer(data_np)
             
-            # 生产环境必须注释！每秒50次打印会导致CPU占用飙升30%以上
-            algo_log(f"数据写入成功：shape={data_np.shape}, 范围=[{data_np.min():.2f}, {data_np.max():.2f}] μV", level="DEBUG", record_once=True)
+            # 写入范式缓冲区
+            with self.paradigmBufferLock:
+                if self.interval_inited:
+                    self.epoch_finished = self.detect_event(data_np)
+                    if self.pack_contain_event:
+                        self.paradigmBuffer.resetAllPara()
+                    self.paradigmBuffer.appendBuffer(data_np)
+                    
+                    if self.epoch_finished:
+                        now = datetime.datetime.now()
+                        time_diff_str = ""
+                        # 计算与上一次Epoch完成的时间差
+                        if self.last_epoch_finish_time is not None:
+                            # 时间差 单位：秒，保留3位小数
+                            delta_seconds = (now - self.last_epoch_finish_time).total_seconds()
+                            time_diff_str = f" | 与上一次间隔: {delta_seconds:.3f} s"
+                        
+                        # 拼接日志，增加时间差信息
+                        log_msg = f"Epoch采集完成: {now.strftime('%H:%M:%S.%f')[:-3]}{time_diff_str}"
+                        algo_log(log_msg, level="DEBUG")
+                        
+                        # 更新上一次Epoch完成时间为当前时间
+                        self.last_epoch_finish_time = now
+                else:
+                    self.paradigmBuffer.appendBuffer(data_np)
 
         except Exception as e:
-            algo_log(f"数据处理失败：{str(e)}", level="ERROR")
-            # 调试阶段临时打开，生产环境务必注释
+            algo_log(f"数据处理失败: {str(e)}", level="ERROR")
+            if IniRead('system', 'algo_log_level', 'INFO') == 'DEBUG':
                 import traceback
                 traceback.print_exc()
 
-    def _process_send_queue(self):
-        """处理发送队列，向所有命令客户端广播消息"""
-        while not self.send_queue.empty():
-            method, params = self.send_queue.get()
-            if self.cmd_clients:
-                try:
-                    msg = {'method': method, 'params': params}
-                    msg_bytes = json.dumps(msg).encode('utf-8')
-                    
-                    # 打印日志（隐藏大尺寸数据）
-                    if method in ['single_trial_plot', 'miReport']:
-                        print(f"{{'method': '{method}', 'params': <Base64 Image Data>}}")
+    # -------------------------- 事件检测 --------------------------
+    def detect_event(self, samples):
+        self.pack_contain_event = False
+        # 第65通道为事件通道
+        event = int(samples[-2][0])
+        # for idx, event in enumerate(events):
+        if event in self.events:
+            new_key = "".join(
+                [
+                    str(event),
+                    datetime.datetime.now().strftime("%Y-%m-%d \
+                                            -%H-%M-%S"),
+                ]
+            )
+            self.currentLabel = event
+            if event == self.predict_event:
+                self.count_events[new_key] = self.latency + 1
             else:
-                        print(f"Sending CMD message: {msg}")
+                self.count_events[new_key] = self.train_latency + 1
+            self.event_inner_idx = self.device_info['frame_points'] - 1
+            # algo_log(f"事件检测到: {event}，索引: {idx}", level="DEBUG")
+            self.pack_contain_event = True   
                 
-                    # 广播到所有命令客户端
-                    for client_id in list(self.cmd_clients):
-                        try:
-                            self.cmd_socket.send_multipart([client_id, b'', msg_bytes])
-                        except Exception as e:
-                            print(f"Error sending to CMD client {client_id}: {e}")
-                            self.cmd_clients.discard(client_id)  # 移除失效客户端
-                except Exception as e:
-                    print(f"Error preparing broadcast: {e}")
+        # 倒计时并清理过期事件
+        drop_items = []
+        for key, value in self.count_events.items():
+            value -= 1
+            if value == 0:
+                drop_items.append(key)
+            self.count_events[key] = value
             
+        for key in drop_items:
+            del self.count_events[key]
+            
+        if drop_items:
+            return True
+        return False
+    # -------------------------- 主循环 --------------------------
     def run(self):
         self.running = True
-        print(f"ZMQ Server started - CMD Port: {self.cmd_port}, DATA Port: {self.data_port}")
+        algo_log(f"ZMQ服务器启动成功 - host: {self.host}, 命令端口: {self.cmd_port}, 数据端口: {self.data_port}", level="INFO")
         
         try:
             while self.running:
-                # 1. 处理发送队列（命令端口广播）
-                self._process_send_queue()
+                # 1. 处理两个端口的发送队列（必须在主线程执行）
+                self._process_cmd_send_queue()
+                self._process_data_send_queue()
 
-                # 2. 轮训监听两个Socket的输入事件（10ms超时，避免阻塞）
-                socks = dict(self.poller.poll(10))
+                # 2. 轮询监听两个端口的输入事件
+                socks = dict(self.poller.poll(50))
 
-                # 处理命令端口消息
+                # 处理8099命令端口消息
                 if self.cmd_socket in socks and socks[self.cmd_socket] == zmq.POLLIN:
                     frames = self.cmd_socket.recv_multipart()
                     self._handle_cmd_message(frames)
 
-                # 处理数据端口消息
+                # 处理8100数据端口消息（排空积压，消除标签延迟）
                 if self.data_socket in socks and socks[self.data_socket] == zmq.POLLIN:
-                    frames = self.data_socket.recv_multipart()
+                    while True:
+                        try:
+                            frames = self.data_socket.recv_multipart(zmq.NOBLOCK)
                             self._handle_data_message(frames)
+                        except zmq.Again:
+                            break
 
         except Exception as e:
-            print(f"Server error occurred: {e}")
+            algo_log(f"服务器主循环异常: {str(e)}", level="ERROR")
+            return
         finally:
             self.running = False
-            # 关闭所有Socket和上下文
+            # 优雅关闭所有资源
             self.cmd_socket.close()
             self.data_socket.close()
             self.context.term()
-            print("Server sockets and context closed.")
+            algo_log("ZMQ服务器已关闭", level="INFO")
 
     def stop(self):
         """显式关闭服务器"""
@@ -243,10 +437,10 @@ class zmqServer(threading.Thread):
         self.cmd_socket.close()
         self.data_socket.close()
         self.context.term()
-        print(f"Server closed explicitly - CMD Port: {self.cmd_port}, DATA Port: {self.data_port}")
+        algo_log(f"服务器已显式关闭 - 命令端口: {self.cmd_port}, 数据端口: {self.data_port}", level="INFO")
 
 if __name__ == '__main__':
-    # 初始化并启动服务器（默认cmd=8099, data=8100）
+    # 初始化并启动服务器
     server = zmqServer()
     server.start()
     
@@ -255,5 +449,5 @@ if __name__ == '__main__':
         while server.running:
             threading.Event().wait(1)
     except KeyboardInterrupt:
-        print("Received KeyboardInterrupt, stopping server...")
+        algo_log("收到键盘中断信号，正在停止服务器...", level="INFO")
         server.stop()

Zmq/zmqServer1.py

@@ -1,445 +0,0 @@
-import numpy as np
-import zmq
-import threading
-import json
-import queue
-import time
-from Device.SunnyLinker import SunnyLinker64, RingBuffer
-from collections import deque
-
-
-class zmqServer(threading.Thread):
-    def __init__(self, host='0.0.0.0', cmd_port=8099, data_port=8100):
-        threading.Thread.__init__(self)
-        self.host = host
-        self.cmd_port = cmd_port
-        self.data_port = data_port
-        self.running = False
-        self.get_Impedance = False
-        self.open_Impedance = None
-        self.StartDecode = False
-        self.StartTrain = False
-        self.state_mode = None
-        self.currentLabel = -1
-        self.IsExitApp = False
-        self.getReport = False
-        self.daemon = True
-
-        # ZMQ Context
-        self.context = zmq.Context()
-
-        # 指令通道 (8099) - ROUTER
-        self.cmd_socket = self.context.socket(zmq.ROUTER)
-        self.cmd_socket.setsockopt(zmq.RCVHWM, 1000)
-        self.cmd_socket.setsockopt(zmq.SNDHWM, 1000)
-        self.cmd_socket.bind(f"tcp://{self.host}:{cmd_port}")
-
-        # 数据通道 (8100)) - ROUTER
-        self.data_socket = self.context.socket(zmq.ROUTER)
-        self.data_socket.setsockopt(zmq.RCVHWM, 1000)
-        self.data_socket.setsockopt(zmq.RCVTIMEO, 50)
-        self.data_socket.bind(f"tcp://{self.host}:{data_port}")
-
-        self.targetFreqs = []
-        self.changeTarget = False
-        self.sunnyLinker = SunnyLinker64(None, None, None, None, None)
-        self.labels = [0x01, 0x02, 0x03]
-
-        self.decoder_switch = False
-        self.decoder_class = None
-        self.cmd_clients = set()
-        self.data_clients = set()
-        self.send_queue = queue.Queue()
-
-        # ========== 数据缓冲区 (RingBuffer) ==========
-        # 与 SunnyLinker 保持一致，使用 RingBuffer
-        # 66 = 64 EEG通道 + 1 事件通道(第65) + 1 标签序号通道(第66)
-        # 缓存约 10 秒数据 (250Hz * 10s = 2500 点)
-        self.n_chan = 66
-        self.t_buffer = 10.0  # 缓冲区时长(秒)
-        self.__ringBuffer = RingBuffer(self.n_chan, int(self.t_buffer * 250))
-
-        # 事件检测相关
-        self._event_lock = threading.Lock()
-        self._epoch_finished = False
-        self._event_inner_idx = -1
-        self.pack_contain_event = False
-        self.predict_event = 99
-        self.events = [1, 2, self.predict_event]
-        self.count_events = {}
-        self.latency = 50
-        self.train_latency = 50
-
-        # 当前事件标签序号 (从第66通道获取)
-        self.current_label_index = 0
-
-        # 初始化标志
-        self._interval_inited = False
-        self._currentLabel = -1
-
-        # 注册的客户端（兼容旧接口）
-        self.clients = set()
-
-    # ========== 事件属性：线程安全访问 ==========
-    @property
-    def epoch_finished(self):
-        with self._event_lock:
-            return self._epoch_finished
-
-    @epoch_finished.setter
-    def epoch_finished(self, value):
-        with self._event_lock:
-            self._epoch_finished = value
-
-    @property
-    def event_inner_idx(self):
-        with self._event_lock:
-            return self._event_inner_idx
-
-    @event_inner_idx.setter
-    def event_inner_idx(self, value):
-        with self._event_lock:
-            self._event_inner_idx = value
-
-    @property
-    def interval_inited(self):
-        return self._interval_inited
-
-    @interval_inited.setter
-    def interval_inited(self, value):
-        self._interval_inited = value
-
-    @property
-    def currentLabel(self):
-        return self._currentLabel
-
-    @currentLabel.setter
-    def currentLabel(self, value):
-        self._currentLabel = value
-
-    def broadcast_message(self, method, params):
-        """Put message into queue to be sent to all connected clients"""
-        self.send_queue.put((method, params))
-
-    # ========== 数据缓冲区操作接口 ==========
-    def GetDataLenCount(self):
-        """返回缓冲区当前数据点数"""
-        return self.__ringBuffer.nUpdate
-
-    def getData(self, count):
-        """获取最新count个数据点，不消费（只读）"""
-        with self.__ringBuffer.RingBufferLock:
-            count = min(count, self.__ringBuffer.nUpdate)
-            if count == 0:
-                return np.zeros((self.n_chan, 0))
-
-            # 计算读取范围（从尾部取最新数据）
-            read_end = (self.__ringBuffer.currentPtr - 1) % self.__ringBuffer.n_points
-            read_start = (read_end - count + 1) % self.__ringBuffer.n_points
-
-            if self.__ringBuffer.currentPtr == 0:
-                read_start = self.__ringBuffer.n_points - count
-                read_end = self.__ringBuffer.n_points - 1
-
-            if read_start <= read_end:
-                data = self.__ringBuffer.buffer[:, read_start:read_end + 1]
-            else:
-                part1 = self.__ringBuffer.buffer[:, read_start:]
-                part2 = self.__ringBuffer.buffer[:, :read_end + 1]
-                data = np.concatenate((part1, part2), axis=1)
-
-            return data
-
-    def consumeData(self, count):
-        """消费(丢弃)指定数量的数据点，从头部移除"""
-        with self.__ringBuffer.RingBufferLock:
-            count = min(count, self.__ringBuffer.nUpdate)
-            self.__ringBuffer.readPtr = (self.__ringBuffer.readPtr + count) % self.__ringBuffer.n_points
-            self.__ringBuffer.nUpdate -= count
-
-    def ResetAll(self):
-        """重置缓冲区"""
-        with self.__ringBuffer.RingBufferLock:
-            self.__ringBuffer.resetAllPara()
-        with self._event_lock:
-            self._epoch_finished = False
-            self._event_inner_idx = -1
-        self.pack_contain_event = False
-        self.count_events.clear()
-        self.current_label_index = 0
-
-    def reset_data_buffer(self):
-        self.ResetAll()
-
-    def reset_state(self):
-        self.ResetAll()
-
-    def interval_init(self, decoder_class):
-        """初始化事件检测参数"""
-        import ast
-        from PubLibrary.InifileHelper import IniRead
-
-        if decoder_class == 'ssmvep':
-            interval_epoch = ast.literal_eval(IniRead('system', 'SSMVEP_IntervalEpoch'))
-            self.interval_epoch = [int(i * 250) for i in interval_epoch]
-            self.train_epoch = [int(self.interval_epoch[0]),
-                                int(self.interval_epoch[1] + 0.1 * 250)]
-            self.latency = (self.interval_epoch[1] + 0.1 * 250) // 5
-            self.train_latency = (self.train_epoch[1] + 0.1 * 250) // 5
-
-        elif decoder_class == 'mi':
-            interval_epoch = ast.literal_eval(IniRead('system', 'MI_IntervalEpoch'))
-            self.interval_epoch = [int(i * 250) for i in interval_epoch]
-            self.train_epoch = self.interval_epoch.copy()
-            self.latency = self.interval_epoch[1] // 5
-            self.train_latency = self.latency
-
-        self.count_events = {}
-        self._event_inner_idx = -1
-        self._epoch_finished = False
-        self.pack_contain_event = False
-        self.predict_event = 99
-        self.events = [1, 2, self.predict_event]
-        self._interval_inited = True
-
-    # ========== 事件检测 ==========
-    def detect_event(self, data_matrix):
-        """
-        检测事件通道中的触发信号
-
-        @param data_matrix: shape (66, N) - N个采样点的数据
-                           第65行(索引64) = 事件通道
-                           第66行(索引65) = 标签通道
-        @return: 是否检测到事件
-        """
-        if data_matrix.shape[1] == 0:
-            return False
-
-        self.pack_contain_event = False
-        event_channel = data_matrix[64, :]   # 第65通道 = 标签值(event值)
-        label_channel = data_matrix[65, :]  # 第66通道 = 标签序号(label index)
-
-        events = event_channel.tolist()
-
-        with self._event_lock:
-            self._event_inner_idx = -1
-            self.current_event_label = 0
-
-            for idx, event in enumerate(events):
-                if int(event) in self.events:
-                    self._event_inner_idx = idx
-                    self.current_label_index = int(label_channel[idx])
-                    self.pack_contain_event = True
-
-                    new_key = f"{event}_{time.time()}"
-                    latency = self.latency if event == self.predict_event else self.train_latency
-                    self.count_events[new_key] = latency + 1
-
-            # 延迟计数递减
-            drop_items = []
-            for key, value in self.count_events.items():
-                value = value - 1
-                if value == 0:
-                    drop_items.append(key)
-                self.count_events[key] = value
-            for key in drop_items:
-                del self.count_events[key]
-
-            if drop_items:
-                self._epoch_finished = True
-                # 检测到事件时，清除RingBuffer中之前的数据，只保留当前包
-                if self.pack_contain_event:
-                    self.__ringBuffer.resetAllPara()
-                return True
-
-            self._epoch_finished = False
-            return False
-
-    def run(self):
-        self.running = True
-        print(f"Server running - CMD: {self.cmd_port}, DATA: {self.data_port}")
-
-        cmd_poller = zmq.Poller()
-        cmd_poller.register(self.cmd_socket, zmq.POLLIN)
-
-        data_poller = zmq.Poller()
-        data_poller.register(self.data_socket, zmq.POLLIN)
-
-        try:
-            while self.running:
-                # --- 处理发送队列 (指令通道) ---
-                while not self.send_queue.empty():
-                    method, params = self.send_queue.get()
-                    if self.cmd_clients:
-                        try:
-                            msg = {'method': method, 'params': params}
-                            msg_bytes = json.dumps(msg).encode('utf-8')
-                            for client_id in list(self.cmd_clients):
-                                try:
-                                    self.cmd_socket.send_multipart([client_id, b'', msg_bytes])
-                                except Exception:
-                                    pass
-                        except Exception:
-                            pass
-
-                # --- 处理指令通道 ---
-                socks = dict(cmd_poller.poll(10))
-                if self.cmd_socket in socks:
-                    self._handle_cmd_socket()
-
-                # --- 处理数据通道 ---
-                socks = dict(data_poller.poll(10))
-                if self.data_socket in socks:
-                    self._handle_data_socket()
-
-        except Exception as e:
-            print(f"Server error: {e}")
-        finally:
-            self.running = False
-            self.cmd_socket.close()
-            self.data_socket.close()
-            self.context.term()
-
-    def _handle_cmd_socket(self):
-        """处理指令通道消息"""
-        try:
-            frames = self.cmd_socket.recv_multipart()
-            if len(frames) < 3:
-                return
-            ident, _, message_bytes = frames[:3]
-            self.cmd_clients.add(ident)
-            self.clients.add(ident)
-
-            message = json.loads(message_bytes.decode('utf-8'))
-            method = message.get("method")
-            params = message.get("params")
-
-            print(f"[CMD] {method}: {params}")
-
-            if method == "sync":
-                self.state_mode = 'sync'
-            elif method == "targetFreqs":
-                if isinstance(params, list) and params != self.targetFreqs:
-                    self.targetFreqs = params
-                    self.changeTarget = True
-            elif method == "decoderClass":
-                if isinstance(params, str) and params != self.decoder_class:
-                    self.decoder_class = params
-                    self.decoder_switch = True
-            elif method == "getReport":
-                self.getReport = True
-            elif method == "train":
-                self.state_mode = 'train'
-                self.StartTrain = True
-                self.currentLabel = params
-            elif method == "predict":
-                self.state_mode = 'predict'
-                if params == 1:
-                    self.StartDecode = True
-                elif params == 2:
-                    self.IsExitApp = True
-                    self.running = False
-            elif method == "rest":
-                self.state_mode = 'rest'
-            elif method == "impedance":
-                if params == 1:
-                    self.open_Impedance = True
-                    self.get_Impedance = True
-                elif params == 2:
-                    self.open_Impedance = False
-                    self.get_Impedance = False
-
-        except Exception as e:
-            print(f"CMD socket error: {e}")
-
-    def _handle_data_socket(self):
-        """处理数据通道消息 (EEG数据)
-
-        上位机数据格式:
-        - 数据帧: [identity, '', meta_json, data_buffer]
-                   data_buffer = [N, 66] float32 -> 转置为 [66, N]
-        """
-        try:
-            frames = self.data_socket.recv_multipart()
-            if len(frames) < 4:
-                return
-            ident, _, message_bytes = frames[:3]
-            self.data_clients.add(ident)
-
-            meta = json.loads(message_bytes.decode('utf-8'))
-
-            # data: [N, 66] -> 转置 -> [66, N]
-            raw_data = np.frombuffer(frames[3], dtype=np.float32)
-            n_samples, n_channels = meta.get('shape', [5, 66])
-            data_matrix = raw_data.reshape(n_samples, n_channels).T.astype(np.float32)
-
-            # 写入 RingBuffer
-            with self.__ringBuffer.RingBufferLock:
-                self.__ringBuffer.appendBuffer(data_matrix)
-
-            # 事件检测
-            self.detect_event(data_matrix)
-
-        except Exception as e:
-            print(f"DATA socket error: {e}")
-
-    # ========== 各范式数据访问接口 ==========
-    def get_MIData(self):
-        """获取MI导联数据 (21通道 + 事件)"""
-        data = self.getData(self.GetDataLenCount())
-        rows_to_extract = [8, 15, 12, 14, 18, 23, 16, 59, 50, 58, 17, 45, 29, 11, 10, 19, 20, 61, 51, 60, 21, 64, 65]
-        row_to_select = np.array(rows_to_extract)
-        if data.shape[1] > 0:
-            return data[row_to_select, :]
-        return np.zeros((len(rows_to_extract), 0))
-
-    def get_SSMVEPData(self):
-        """获取SSMVEP导联数据 (8通道 + 事件)"""
-        data = self.getData(self.GetDataLenCount())
-        rows_to_extract = [13, 3, 2, 46, 9, 54, 47, 55, 64, 65]
-        row_to_select = np.array(rows_to_extract)
-        if data.shape[1] > 0:
-            return data[row_to_select, :]
-        return np.zeros((len(rows_to_extract), 0))
-
-    def getDataViaSSVEP(self, count):
-        """获取SSVEP数据 (8通道 + 事件)"""
-        data = self.getData(count)
-        rows_to_extract = [13, 3, 2, 46, 9, 54, 47, 55, 64]
-        row_to_select = np.array(rows_to_extract)
-        if data.shape[1] > 0:
-            return data[row_to_select, :]
-        return np.zeros((len(rows_to_extract), 0))
-
-    def get_concentrateData(self, count):
-        """获取专注力数据 (2通道)"""
-        data = self.getData(count)
-        rows_to_extract = [0, 1]
-        row_to_select = np.array(rows_to_extract)
-        if data.shape[1] > 0:
-            return data[row_to_select, :]
-        return np.zeros((len(rows_to_extract), 0))
-
-    def get_blinkData(self, count):
-        """获取眨眼数据 (2通道)"""
-        data = self.getData(count)
-        rows_to_extract = [0, 1]
-        row_to_select = np.array(rows_to_extract)
-        if data.shape[1] > 0:
-            return data[row_to_select, :]
-        return np.zeros((len(rows_to_extract), 0))
-
-    def getImpedance(self, data, decoder_class):
-        """计算阻抗（ZMQ模式下不可用）"""
-        return np.zeros(8)
-
-    def stop(self):
-        self.running = False
-        self.cmd_socket.close()
-        self.data_socket.close()
-        self.context.term()
-
-
-if __name__ == '__main__':
-    server = zmqServer()
-    server.start()

concentration/algorithm/calculate_focus.py

@@ -8,6 +8,7 @@ import os
 # import logging
 import base64
 import io
+import math
 
 # logger = logging.getLogger(__name__)
 #
@@ -22,7 +23,7 @@ import io
 
 
 class Calculate():
-    def __init__(self, Threshold_value_low, Threshold_value_high, fs=250, win_len=10):
+    def __init__(self, Threshold_value_low, Threshold_value_high, fs=250, win_len=10, config=None):
         self.Threshold_value_low = Threshold_value_low
         self.Threshold_value_high = Threshold_value_high
         self.fs = fs
@@ -31,47 +32,73 @@ class Calculate():
         self.EVI_result = []
         self.eegQueue = deque(maxlen=win_len)
 
-        # # 存储历史数据用于绘图
-        # self.beta_history = []
-        # self.alpha_history = []
-        # self.theta_history = []
-        # self.focus_history = []
-        # self.timestamp_history = []
-        #
-        # # 记录开始时间
-        # self.start_time = None
-        # self.recording = False
-        #
-        # # 图表保存路径
-        # self.chart_dir = "reports"
-        # if not os.path.exists(self.chart_dir):
-        #     os.makedirs(self.chart_dir)
-        #     print(f"[调试] 创建目录: {self.chart_dir}")
-        
         # 初始化滤波器
         self.b_notch, self.a_notch = signal.iirnotch(50 / (self.fs/2), 30)
         self.b_design = signal.firwin(65, [2 / (self.fs/2), 40 / (self.fs/2)], pass_zero=False)
 
+        self.last_focus = None
+        # 异步滤波系数配置（核心手感控制纽）
+        self.alpha_up = 1  # 上升系数：较小，保证分数平滑爬升，过滤偶发的瞬时高能量
+        # alpha_down / shrink_factor 从 config.ini 读取，方便上位机调参
+        if config:
+            self.alpha_down = float(config.get('alpha_down', 0.8))
+            self.shrink_factor = float(config.get('shrink_factor', 0.5))
+        else:
+            self.alpha_down = 0.8
+            self.shrink_factor = 0.5
         print("[调试] Calculate 类初始化完成")
 
     def calculate_focus(self, beta, alpha, theta):
         """
-        专注度计算 - 固定映射版本
+        专注度计算 - 三区间门限异步滤波版本
         """
+        # 0. 频带特征预处理
+        theta_mod = theta ** 0.7
+
         # 原始比值
-        raw = beta / (alpha + theta + 1e-10)
+        raw = beta / (alpha + theta_mod + 1e-10)
 
-        # Sigmoid 映射：让 raw 在 0.3-1.5 区间敏感
-        # 参数可调：
-        #   k = 12  (斜率，越大越陡)
-        #   x0 = 0.6 (中心点，raw=0.6时focus≈50)
-        k = 12.0
-        x0 = 0.6
-        focus = 100.0 / (1.0 + np.exp(-k * (raw - x0)))
+        exponent = 2.0
 
-        # 可选：添加滑动平均平滑
+        # 1. 防止脑电比值出现负数异常值
+        raw_input = max(raw, 0.0)
+
+        # 2. 2次幂纵轴压缩映射 (shrink_factor 从 config.ini 读取)
+        focus_raw = 100 * self.shrink_factor * (raw_input ** exponent)
+
+        # 3. 计算当前帧的瞬时分数 (基准量级 0-120)
+        instant_focus = 120 * (1.0 - np.exp(-focus_raw / 100.0))
+
+        # 4. 核心修改：三区间门限时域滤波
+        if self.last_focus is None:
+            # 冷启动：首帧直接赋值
+            focus = instant_focus
+        else:
+            # 判断当前瞬时分数是否处于【极端区】(80以上 或 60以下)
+            if instant_focus > 85.0 or instant_focus < 60.0:
+                # 执行异步低通时域滤波
+                if instant_focus >= self.last_focus:
+                    # 趋势上升：慢爬升
+                    focus = self.alpha_up * instant_focus + (1 - self.alpha_up) * self.last_focus
+                else:
+                    # 趋势下降：快跌落
+                    focus = self.alpha_down * instant_focus + (1 - self.alpha_down) * self.last_focus
+            else:
+                # 【高灵敏自由区】(60 <= instant_focus <= 80)
+                # 不执行异步滤波，分数直接跟随瞬时值，保证中间状态绝对跟手
+                focus = instant_focus
+
+        # 5. 更新历史状态缓存
+        self.last_focus = focus
+
+        # 打印在线调试日志，方便观察区间切换
+        zone_tag = "极端区(滤波)" if (instant_focus > 80 or instant_focus < 60) else "自由区(直通)"
+        print(f"原始特征比值 raw: {raw:.4f} | 瞬时分数: {instant_focus:.1f} | 滤波后分数: {focus:.1f}")
+
+        # 最终返回整型
         return int(focus)
 
+
     def calculate_all(self, data, fs, nperseg=1000):
         mean_x = np.mean(data, axis=-1, keepdims=True)
         data = data - mean_x
@@ -319,14 +346,16 @@ class Calculate():
             if eegData.size == 0:
                 return None
             eegData -= np.mean(eegData, axis=-1, keepdims=True)
-            eegData = signal.lfilter(self.b_notch, self.a_notch, eegData)
-            eegData = signal.lfilter(self.b_design, 1, eegData)
-            focus_score, CLI_score, beta, alpha, theta = self.calculate_all(eegData, fs=self.fs, nperseg=1000)
+            # eegData = signal.lfilter(self.b_notch, self.a_notch, eegData)    # 陷波
+            # eegData = signal.lfilter(self.b_design, 1, eegData)              # 滤波
+            focus_score, CLI_score, beta_psd, alpha_psd, theta_psd = self.calculate_all(eegData, fs=self.fs, nperseg=1000)
 
-            # self.add_data_point(focus_score, beta, alpha, theta)
+            # self.add_data_point(focus_score, beta_psd, alpha_psd, theta_psd)  # 已注释（方法已移除）
+
+            # return (focus_score)
+            return (focus_score, beta_psd)
+        # return None
 
-            return focus_score
-        return None
 
 
 class Calculate2():

config.ini

@@ -15,14 +15,19 @@ Audio_device = 0
 Rest_time = 2
 Upper_Host = 127.0.0.1
 Upper_Port = 8088
+Decoder_Host = 127.0.0.1
+Decoder_Port = 8099
 Serial_port = COM44
+algo_log_path = d:/Program Files/64chn_Decoder/logs
 algo_log_level = DEBUG
 console_output = 1
+save_train_data = 0
+zmqServer_host = 127.0.0.1
 
-
-; 64 导设备配置 1; 32 2; 24 3; 16 4; 8 5; 4 6;
-[device_type] = 1
-device_sample_rate = 250
-device_channel_nums = 66
-device_channel_names = ['FP1', 'FP2', 'PO6', 'POZ', 'F3', 'F4', 'FPZ', 'AF4', 'FC3', 'PO8', 'CP2', 'CP1', 'FCZ', 'PO5', 'FC2', 'FC1', 'C3', 'C4', 'FC4', 'CP4', 'P3', 'P4', 'F5', 'C5', 'F6', 'PO4', 'CP6', 'CP5', 'PO3', 'CP3', 'FC6', 'FC5', 'CB1', 'CB2', 'P5', 'AF7', 'A1', 'T7', 'FT7', 'TP7', 'FT8', 'AF8', 'F8', 'F7', 'P6', 'C6', 'O2', 'O1', 'T8', 'P7', 'CZ', 'PZ', 'P8', 'FZ', 'OZ', 'PO7', 'TP8', 'AF3', 'C2', 'C1', 'P2', 'P1', 'F2', 'F1', 'label', 'label_tag']
-device_channel_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]
+; 64 导设备配置
+[device_type_1]
+sample_rate = 250
+frame_points = 5
+channel_nums = 66
+channel_names = ['FP1', 'FP2', 'PO6', 'POZ', 'F3', 'F4', 'FPZ', 'AF4', 'FC3', 'PO8', 'CP2', 'CP1', 'FCZ', 'PO5', 'FC2', 'FC1', 'C3', 'C4', 'FC4', 'CP4', 'P3', 'P4', 'F5', 'C5', 'F6', 'PO4', 'CP6', 'CP5', 'PO3', 'CP3', 'FC6', 'FC5', 'CB1', 'CB2', 'P5', 'AF7', 'A1', 'T7', 'FT7', 'TP7', 'FT8', 'AF8', 'F8', 'F7', 'P6', 'C6', 'O2', 'O1', 'T8', 'P7', 'CZ', 'PZ', 'P8', 'FZ', 'OZ', 'PO7', 'TP8', 'AF3', 'C2', 'C1', 'P2', 'P1', 'F2', 'F1', 'label', 'label_tag']
+channel_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]

datamock.py

@@ -0,0 +1,188 @@
+import zmq
+import numpy as np
+import time
+import threading
+from datetime import datetime
+
+# ========== 参数配置 ==========
+FS = 250              # 采样率 Hz
+N_SAMPLES_PER_PKT = 5  # 每包采样点数
+N_CHAN = 66           # 通道数: 64 EEG + 1 标签值 + 1 标签序号
+EEG_FREQ = 10         # EEG 正弦波频率 Hz
+EEG_AMP = 100.0      # EEG 幅值 100μV
+LABEL_INTERVAL = 5    # 标签间隔秒数
+SERVER_ADDR = 'tcp://127.0.0.1:8100'
+LABEL_CMD_ADDR = 'tcp://127.0.0.1:8101'  # 接收来自上位机范式的标签命令
+
+# 发送间隔: 每包 5 采样点 / 250Hz = 20ms
+PKT_INTERVAL = N_SAMPLES_PER_PKT / FS
+
+
+def build_packet(global_sample_idx):
+    """
+    生成一包 [5, 66] 的 float64 数据
+    :param global_sample_idx: 当前包第一个采样点在全局序列中的索引 (从 0 开始)
+    :return: np.ndarray shape [5, 66]
+    """
+    # 当前包内 5 个采样点对应的时间（秒）
+    t = (global_sample_idx + np.arange(N_SAMPLES_PER_PKT)) / FS
+
+    # Ch0-63: EEG 10Hz 正弦波，幅值 100μV
+    # t shape [5,]，sin 乘以标量后仍是 [5,]，需要 reshape 为 [5,1] 再广播到 64 通道
+    eeg = (EEG_AMP * np.sin(2 * np.pi * EEG_FREQ * t)).reshape(N_SAMPLES_PER_PKT, 1)  # [5, 1]
+    eeg = np.tile(eeg, (1, 64))  # [5, 64]
+
+    # Ch64: 标签值通道，初始化为 0
+    event = np.zeros((N_SAMPLES_PER_PKT, 1), dtype=np.float64)
+
+    # Ch65: 标签序号通道，初始化为 0
+    label_idx = np.zeros((N_SAMPLES_PER_PKT, 1), dtype=np.float64)
+
+    # 拼成 [5, 66]
+    packet = np.concatenate([eeg, event, label_idx], axis=1).astype(np.float64)
+    return packet
+
+
+def should_send_label(global_sample_idx):
+    """
+    判断当前包是否包含标签触发点（每 5s 的最后一个采样点）
+    采样点索引从 0 开始，每 5s = 1250 个采样点
+    最后一个采样点索引: 1249, 2499, 3749, ...
+    由于每包 5 个采样点，标签点落在包内的最后一个采样点位置
+    即当前包起始索引 global_sample_idx 必须使得:
+        global_sample_idx <= 标签点索引 < global_sample_idx + N_SAMPLES_PER_PKT
+    也就是 global_sample_idx <= 1249 < global_sample_idx + 5
+    即 global_sample_idx = 1245, 2495, 3745, ...
+    即 global_sample_idx = n * LABEL_INTERVAL * FS - N_SAMPLES_PER_PKT
+    """
+    samples_per_interval = LABEL_INTERVAL * FS
+    # 检查当前包是否包含 interval 的最后一个采样点
+    # 标签点索引 = n * 1250 - 1，当 global_sample_idx = n*1250-5 时，标签在包内索引 4
+    return (global_sample_idx + N_SAMPLES_PER_PKT - 1) % samples_per_interval == samples_per_interval - 1
+
+
+def main():
+    ctx = zmq.Context()
+    sock = ctx.socket(zmq.DEALER)
+    sock.connect(SERVER_ADDR)
+    print(f"[{datetime.now().strftime('%H:%M:%S')}] ZMQ Dealer 连接到 {SERVER_ADDR}")
+
+    # ========== 上位机标签命令监听 ==========
+    # 使用线程安全的队列接收来自 ssmvep_main.py 的标签命令
+    # 标签值: 1 (train 0), 2 (train 1), 99 (predict)
+    pending_label = [None]       # [label_value or None]
+    label_lock = threading.Lock()
+
+    label_cmd_sock = ctx.socket(zmq.PULL)
+    label_cmd_sock.bind(LABEL_CMD_ADDR)
+    print(f"[{datetime.now().strftime('%H:%M:%S')}] 标签命令监听绑定到 {LABEL_CMD_ADDR}")
+
+    stop_recv = threading.Event()
+
+    def label_cmd_thread():
+        """监听来自上位机范式的标签命令，写入 pending_label"""
+        while not stop_recv.is_set():
+            try:
+                msg = label_cmd_sock.recv_string(zmq.NOBLOCK)
+                label_val = int(msg)
+                with label_lock:
+                    pending_label[0] = label_val
+                ts = datetime.now().strftime('%H:%M:%S')
+                label_name = {1: 'train_0', 2: 'train_1', 99: 'predict'}.get(label_val, str(label_val))
+                print(f"[{ts}] 收到标签命令: {label_name} -> label={label_val}")
+            except zmq.Again:
+                time.sleep(0.005)
+            except Exception as e:
+                print(f"[label_cmd_thread] 错误: {e}")
+                time.sleep(0.01)
+
+    label_thread = threading.Thread(target=label_cmd_thread, daemon=True)
+    label_thread.start()
+    print(f"[{datetime.now().strftime('%H:%M:%S')}] 标签命令监听线程已启动")
+
+    # 后台消费线程：持续 recv 从 ROUTER 返回的数据，避免 server 发送队列积压
+    recv_count = [0]
+
+    def consumer_thread():
+        """消费线程：阻塞 recv，丢弃收到的数据，仅用于清空 ROUTER 发送队列"""
+        while not stop_recv.is_set():
+            try:
+                frames = sock.recv_multipart(zmq.NOBLOCK)
+                recv_count[0] += 1
+                # 收到的格式: [identity, '', filtered_data_bytes]
+                if recv_count[0] % 500 == 0:
+                    print(f"[{datetime.now().strftime('%H:%M:%S')}] 消费线程已丢弃 {recv_count[0]} 帧滤波数据")
+            except zmq.Again:
+                time.sleep(0.01)
+            except zmq.error.Again:  # 兼容旧版
+                time.sleep(0.01)
+
+    consumer = threading.Thread(target=consumer_thread, daemon=True)
+    consumer.start()
+    print(f"[{datetime.now().strftime('%H:%M:%S')}] 消费线程已启动（daemon）")
+
+    global_sample_idx = 0       # 全局采样点计数器
+    label_type = 1              # 当前标签类型: 1 或 2
+    label1_count = 0            # label=1 的序号计数器
+    label2_count = 0            # label=2 的序号计数器
+    packet_count = 0            # 已发送包数
+
+    print(f"[{datetime.now().strftime('%H:%M:%S')}] 开始发送模拟数据 ...")
+    print(f"  采样率: {FS}Hz | 每包 {N_SAMPLES_PER_PKT} 采样点 | 发送间隔 {PKT_INTERVAL*1000:.0f}ms")
+    print(f"  EEG: {EEG_FREQ}Hz 正弦波 | 幅值 {EEG_AMP}μV")
+    print(f"  标签: 来自上位机范式命令 (train_0=1, train_1=2, predict=99)")
+    print("-" * 50)
+
+    try:
+        while True:
+            t_start = time.perf_counter()
+
+            # 构建当前包
+            packet = build_packet(global_sample_idx)
+
+            # 检查是否有来自上位机范式的挂起标签命令
+            with label_lock:
+                ext_label = pending_label[0]
+                if ext_label is not None:
+                    pending_label[0] = None
+
+            if ext_label is not None:
+                # 将标签写入当前包所有5个采样点的第65通道 (index 64)
+                # 覆盖全部采样点确保 event_inner_idx 无论落在哪个位置都能被正确检测
+                packet[:, 64] = float(ext_label)
+                ts = datetime.now().strftime('%H:%M:%S')
+                print(f"[{ts}] 打标签: label={ext_label} -> ch64[all 5 samples] (global_sample_idx={global_sample_idx})")
+
+            # 发送: multipart 2帧 ['', data]
+            # 使用标准格式，ROUTER 会自动附加 ZMQ 分配的客户端身份
+            sock.send_multipart([
+                b'',
+                packet.tobytes()
+            ])
+
+            # 每 50 包打印一次进度
+            if packet_count % 50 == 0:
+                ts = datetime.now().strftime('%H:%M:%S')
+                print(f"[{ts}] 已发送 {packet_count} 包 (global_sample_idx={global_sample_idx})")
+
+            global_sample_idx += N_SAMPLES_PER_PKT
+            packet_count += 1
+
+            # 精确控制发送节奏: 等待到 PKT_INTERVAL 秒
+            elapsed = time.perf_counter() - t_start
+            sleep_time = PKT_INTERVAL - elapsed
+            if sleep_time > 0:
+                time.sleep(sleep_time)
+
+    except KeyboardInterrupt:
+        print(f"\n[{datetime.now().strftime('%H:%M:%S')}] 停止发送，共发送 {packet_count} 包")
+    finally:
+        stop_recv.set()
+        consumer.join(timeout=2)
+        label_cmd_sock.close()
+        sock.close()
+        ctx.term()
+
+
+if __name__ == '__main__':
+    main()

filter_test.py

@@ -0,0 +1,421 @@
+# -*- coding: utf-8 -*-
+"""
+脑电滤波服务 8100端口测试工具【统计逻辑专项优化版】
+优化点：
+1. 5秒预热(250个发包)，预热结束后才启动丢包/数据统计
+2. 业务比例：0.02s发1包，200ms收1包 → 每 10 个发包对应 1 个回包
+3. 通道校验：发送(5,66) 仅对比前64通道，接收(50,64)全通道比对
+4. 区分：全局总包数 / 有效统计区间包数、理论收包数、实际收包数、丢包数、丢包率
+5. 新增64通道整体数据均值/极值比对，校验数据有效性
+通信规范：send_multipart([client_id, b"", data_buf]) 三帧报文，服务端 recv_multipart 长度=3
+"""
+import sys
+import time
+import threading
+import logging
+import traceback
+from collections import deque
+import numpy as np
+import zmq
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+
+# ===================== 全局前置：修复Matplotlib中文字体 & 负号显示 =====================
+plt.rcParams["font.sans-serif"] = ["SimHei", "Microsoft YaHei", "WenQuanYi Micro Hei"]
+plt.rcParams["axes.unicode_minus"] = False
+
+# ===================== 【1. 全局业务固定参数（核心统计规则）】 =====================
+# ZMQ 服务端配置
+ZMQ_SERVER_IP = "127.0.0.1"
+ZMQ_SERVER_PORT = 8100
+ZMQ_SOCKET_TIMEOUT = 3000    # 套接字超时(ms)
+POLL_TIMEOUT = 10            # Poll轮询超时(ms)
+
+# 时序 & 统计核心规则（严格对齐现场业务）
+SEND_INTERVAL = 0.02         # 上位机发包间隔：20ms/包
+RECV_INTERVAL = 0.2          # 服务端回包间隔：200ms/包
+PREHEAT_SECONDS = 5.0       # 滤波缓存预热时长：5秒
+# 计算：预热需要的发包总数 = 预热时长 / 单包发送间隔
+PREHEAT_SEND_PACKS = int(PREHEAT_SECONDS / SEND_INTERVAL)  # 5 / 0.02 = 250 包
+# 收发包比例：每多少个发包对应1个回包
+PACK_RATIO = int(RECV_INTERVAL / SEND_INTERVAL)            # 0.2 / 0.02 = 10
+
+# 数据报文形状
+PKG_SEND_SHAPE = (5, 66)     # 发送包 (点数, 总通道)
+PKG_RECV_SHAPE = (50, 64)    # 回包 (点数, 有效脑电通道)
+SAMPLE_RATE = 250
+
+# 通道定义（对比仅使用前64路脑电通道）
+CH_EEG_VALID = 64            # 共同对比通道数：0~63
+CH_EVENT = 64
+CH_RESERVED = 65
+
+# ZMQ 三帧报文固定字段
+CLIENT_ID = b"test_client_001"
+EMPTY_FRAME = b""
+
+# 仿真信号配置
+TARGET_CHANNEL = 0
+SIGNAL_FREQ_LIST = [13]
+SIGNAL_AMP = 1.8
+NOISE_GAUSSIAN_AMP = 0.4
+NOISE_POWER50_AMP = 0.3
+EVENT_LABEL_VAL = 1
+RESERVED_VAL = 0.0
+
+# 可视化配置
+MAX_PLOT_POINTS = 800
+PLOT_REFRESH_INTERVAL = 80
+FFT_N_POINTS = 256
+PLOT_X_LIMIT_FREQ = (0, 60)
+
+# 运行控制
+MAX_RUN_SECONDS = None
+ENABLE_RECONNECT = True
+PRINT_STAT_INTERVAL = 5.0
+
+# ===================== 【2. 全局变量 + 统计结构体（重构统计逻辑）】 =====================
+g_running = threading.Event()
+g_running.set()
+data_lock = threading.Lock()
+
+# 绘图缓冲区
+raw_data_buf = deque(maxlen=MAX_PLOT_POINTS)
+filt_data_buf = deque(maxlen=MAX_PLOT_POINTS)
+
+# ===================== 全新统计变量（区分预热/正式统计） =====================
+stat = {
+    # 全局总包数（包含预热包）
+    "total_send": 0,
+    "total_recv": 0,
+
+    # 有效统计区间（预热250包之后）
+    "valid_send": 0,        # 有效发包数
+    "valid_recv": 0,        # 有效收包数
+    "theo_recv": 0,         # 理论应收到包数 = valid_send // PACK_RATIO
+
+    # 运行时间
+    "start_time": time.perf_counter(),
+    "last_print_time": time.perf_counter(),
+
+    # 数据校验缓存：保存最新一包原始64通道数据，用于和回包比对
+    "latest_raw_64ch": None
+}
+
+# ===================== 【3. 日志配置】 =====================
+def init_logger():
+    log_format = "%(asctime)s | %(levelname)-8s | %(message)s"
+    logging.basicConfig(
+        level=logging.INFO,
+        format=log_format,
+        datefmt="%Y-%m-%d %H:%M:%S"
+    )
+    return logging.getLogger("FilterTest")
+
+logger = init_logger()
+
+# ===================== 【4. 仿真脑电数据生成 (5,66)】 =====================
+def generate_eeg_packet(pkt_idx: int) -> np.ndarray:
+    """生成单包 (5,66) 仿真数据"""
+    n_point, n_chan = PKG_SEND_SHAPE
+    base_t = pkt_idx * n_point / SAMPLE_RATE
+    t_arr = base_t + np.arange(n_point) / SAMPLE_RATE
+
+    data = np.zeros((n_point, n_chan), dtype=np.float64)
+
+    # 64路脑电信号
+    for ch in range(CH_EEG_VALID):
+        sig = 0.0
+        for freq in SIGNAL_FREQ_LIST:
+            sig += SIGNAL_AMP * np.sin(2 * np.pi * freq * t_arr)
+        # sig += NOISE_POWER50_AMP * np.sin(2 * np.pi * 50 * t_arr)
+        # sig += NOISE_GAUSSIAN_AMP * np.random.randn(n_point)
+        data[:, ch] = sig
+
+    # 事件通道、保留通道
+    data[:, CH_EVENT] = EVENT_LABEL_VAL
+    data[:, CH_RESERVED] = RESERVED_VAL
+    return data
+
+# ===================== 【5. ZMQ 核心IO线程（单连接+Poller，保留原有通信逻辑）】 =====================
+def zmq_io_thread():
+    context = zmq.Context()
+    pkt_index = 0
+    send_interval = SEND_INTERVAL
+
+    logger.info(f"滤波预热配置：{PREHEAT_SECONDS}秒 / {PREHEAT_SEND_PACKS} 个发包后开始统计")
+    logger.info(f"收发比例：每 {PACK_RATIO} 个发包 → 1 个滤波回包")
+
+    while g_running.is_set():
+        try:
+            sock = context.socket(zmq.DEALER)
+            sock.setsockopt(zmq.RCVTIMEO, ZMQ_SOCKET_TIMEOUT)
+            sock.setsockopt(zmq.SNDTIMEO, ZMQ_SOCKET_TIMEOUT)
+            sock.connect(f"tcp://{ZMQ_SERVER_IP}:{ZMQ_SERVER_PORT}")
+            logger.info(f"ZMQ 连接成功 -> {ZMQ_SERVER_IP}:{ZMQ_SERVER_PORT}")
+
+            poller = zmq.Poller()
+            poller.register(sock, zmq.POLLIN)
+            next_send_ts = time.perf_counter()
+
+            while g_running.is_set():
+                # 全局运行时长限制
+                if MAX_RUN_SECONDS is not None:
+                    run_sec = time.perf_counter() - stat["start_time"]
+                    if run_sec > MAX_RUN_SECONDS:
+                        logger.info(f"已到达设定运行时长 {MAX_RUN_SECONDS}s，停止任务")
+                        return
+
+                # ========== 1. 轮询接收服务端回包 ==========
+                socks_ready = dict(poller.poll(POLL_TIMEOUT))
+                if sock in socks_ready:
+                    frames = sock.recv_multipart()
+                    if not frames:
+                        continue
+                    recv_bytes = frames[-1]
+                    if not recv_bytes:
+                        continue
+
+                    # 解析回包 (50,64)
+                    filt_data = np.frombuffer(recv_bytes, dtype=np.float64)
+                    expect_size = PKG_RECV_SHAPE[0] * PKG_RECV_SHAPE[1]
+                    if filt_data.size != expect_size:
+                        logger.warning(f"回包长度异常：实际{filt_data.size}，预期{expect_size}")
+                        continue
+                    filt_data = filt_data.reshape(PKG_RECV_SHAPE)
+
+                    # 全局收包计数
+                    stat["total_recv"] += 1
+
+                    # 仅预热完成后，计入有效统计收包
+                    if stat["total_send"] > PREHEAT_SEND_PACKS:
+                        stat["valid_recv"] += 1
+
+                    # 写入绘图缓冲区
+                    with data_lock:
+                        filt_data_buf.extend(filt_data[:, TARGET_CHANNEL])
+
+                    # ---------- 新增：64通道数据比对（发包前64通道 <-> 回包64通道） ----------
+                    raw_64ch = stat["latest_raw_64ch"]
+                    if raw_64ch is not None:
+                        raw_mean = np.mean(raw_64ch)
+                        filt_mean = np.mean(filt_data)
+                        raw_amp = np.max(np.abs(raw_64ch))
+                        filt_amp = np.max(np.abs(filt_data))
+                        logger.debug(
+                            f"【通道数据比对】原始64通道均值:{raw_mean:.4f} 幅值:{raw_amp:.4f} | "
+                            f"滤波后均值:{filt_mean:.4f} 幅值:{filt_amp:.4f}"
+                        )
+
+                # ========== 2. 精准定时发送数据包 ==========
+                current_ts = time.perf_counter()
+                if current_ts >= next_send_ts:
+                    # 生成(5,66)仿真包
+                    pkt_data = generate_eeg_packet(pkt_index)
+                    pkt_index += 1
+                    send_buf = pkt_data.tobytes()
+
+                    # 标准三帧Multipart发送
+                    sock.send_multipart([CLIENT_ID, EMPTY_FRAME, send_buf])
+
+                    # ---------- 发包计数逻辑（核心优化：预热区分） ----------
+                    stat["total_send"] += 1
+                    # 预热完成后，计入有效发包
+                    if stat["total_send"] > PREHEAT_SEND_PACKS:
+                        stat["valid_send"] += 1
+                        # 计算理论应收包数
+                        stat["theo_recv"] = stat["valid_send"] // PACK_RATIO
+
+                    # 缓存当前包前64通道，用于后续数据比对
+                    stat["latest_raw_64ch"] = pkt_data[:, :CH_EEG_VALID]
+
+                    # 绘图缓冲区（单通道波形）
+                    with data_lock:
+                        raw_data_buf.extend(pkt_data[:, TARGET_CHANNEL])
+
+                    # 更新下一次发包时间
+                    next_send_ts += send_interval
+
+                # ========== 3. 定时打印统计信息（区分预热/正式统计） ==========
+                now = time.perf_counter()
+                if now - stat["last_print_time"] > PRINT_STAT_INTERVAL:
+                    run_sec = now - stat["start_time"]
+                    total_send = stat["total_send"]
+                    total_recv = stat["total_recv"]
+
+                    # 分支1：仍在预热阶段
+                    if total_send <= PREHEAT_SEND_PACKS:
+                        remain = PREHEAT_SEND_PACKS - total_send
+                        logger.info(
+                            f"[预热中] 运行:{run_sec:.1f}s | 已发包:{total_send}/{PREHEAT_SEND_PACKS} | "
+                            f"剩余预热包:{remain} | 暂不统计丢包"
+                        )
+                    # 分支2：预热完成，进入正式统计
+                    else:
+                        v_send = stat["valid_send"]
+                        v_recv = stat["valid_recv"]
+                        t_recv = stat["theo_recv"]
+                        loss_cnt = t_recv - v_recv
+                        loss_rate = (loss_cnt / t_recv * 100) if t_recv > 0 else 0.0
+
+                        logger.info(
+                            f"[正式统计] 运行:{run_sec:.1f}s | "
+                            f"全局总包: 发{total_send}/收{total_recv} | "
+                            f"有效区间: 发{v_send}/应收{t_recv}/实收{v_recv} | "
+                            f"丢包数:{loss_cnt} | 丢包率:{loss_rate:.2f}%"
+                        )
+                    stat["last_print_time"] = now
+
+        except zmq.ZMQError as e:
+            if e.errno == zmq.EAGAIN:
+                continue
+            logger.warning(f"ZMQ 连接异常: {e}")
+            sock.close()
+            poller.unregister(sock)
+            if not ENABLE_RECONNECT:
+                break
+            logger.info("500ms 后尝试重连...")
+            time.sleep(0.5)
+        except Exception as e:
+            logger.error(f"IO线程未知异常:\n{traceback.format_exc()}")
+            break
+
+    context.term()
+    logger.info("ZMQ IO 线程已退出")
+
+# ===================== 【6. 可视化绘图（无改动）】 =====================
+def init_plot():
+    fig = plt.figure(figsize=(14, 9))
+    fig.suptitle(f"脑电滤波测试 | 观测通道: {TARGET_CHANNEL}", fontsize=14)
+
+    ax1 = plt.subplot(2, 2, 1)
+    ax1.set_title("原始输入波形 (含噪声+工频)")
+    ax1.set_ylabel("幅值")
+    ax1.grid(True, alpha=0.3)
+    line_raw, = ax1.plot([], [], color="#1f77b4", linewidth=1)
+
+    ax2 = plt.subplot(2, 2, 2)
+    ax2.set_title("滤波后输出波形")
+    ax2.set_ylabel("幅值")
+    ax2.grid(True, alpha=0.3)
+    line_filt, = ax2.plot([], [], color="#d62728", linewidth=1)
+
+    ax3 = plt.subplot(2, 2, 3)
+    ax3.set_title("原始信号频谱")
+    ax3.set_xlabel("频率 (Hz)")
+    ax3.set_xlim(*PLOT_X_LIMIT_FREQ)
+    ax3.grid(True, alpha=0.3)
+    line_raw_fft, = ax3.plot([], [], color="#1f77b4")
+
+    ax4 = plt.subplot(2, 2, 4)
+    ax4.set_title("滤波后信号频谱")
+    ax4.set_xlabel("频率 (Hz)")
+    ax4.set_xlim(*PLOT_X_LIMIT_FREQ)
+    ax4.grid(True, alpha=0.3)
+    line_filt_fft, = ax4.plot([], [], color="#d62728")
+
+    plt.tight_layout(rect=[0, 0, 1, 0.96])
+    return fig, [line_raw, line_filt, line_raw_fft, line_filt_fft], [ax1, ax2, ax3, ax4]
+
+def update_plot(frame, lines, axes):
+    line_raw, line_filt, line_raw_fft, line_filt_fft = lines
+    ax1, ax2, ax3, ax4 = axes
+
+    with data_lock:
+        raw_data = list(raw_data_buf)
+        filt_data = list(filt_data_buf)
+
+    if raw_data:
+        x_raw = np.arange(len(raw_data))
+        line_raw.set_data(x_raw, raw_data)
+        ax1.relim()
+        ax1.autoscale_view()
+
+    if filt_data:
+        x_filt = np.arange(len(filt_data))
+        line_filt.set_data(x_filt, filt_data)
+        ax2.relim()
+        ax2.autoscale_view()
+
+    def calc_fft(sig, n_fft):
+        if len(sig) < n_fft:
+            return [], []
+        win = np.hanning(n_fft)
+        sig_win = sig[-n_fft:] * win
+        fft_vals = np.fft.fft(sig_win)
+        fft_amp = np.abs(fft_vals)[:n_fft//2]
+        freq = np.fft.fftfreq(n_fft, 1/SAMPLE_RATE)[:n_fft//2]
+        return freq, fft_amp
+
+    freq_raw, amp_raw = calc_fft(raw_data, FFT_N_POINTS)
+    freq_filt, amp_filt = calc_fft(filt_data, FFT_N_POINTS)
+
+    line_raw_fft.set_data(freq_raw, amp_raw)
+    line_filt_fft.set_data(freq_filt, amp_filt)
+    ax3.relim()
+    ax3.autoscale_view(scaley=True)
+    ax4.relim()
+    ax4.autoscale_view(scaley=True)
+
+    return lines
+
+# ===================== 【7. 资源释放 & 最终汇总统计】 =====================
+def clean_resource():
+    g_running.clear()
+    logger.info("开始停止所有线程...")
+    time.sleep(0.3)
+    plt.close("all")
+    logger.info("资源释放完成")
+
+def main():
+    logger.info("=" * 70)
+    logger.info("脑电滤波测试客户端【统计逻辑优化版】启动")
+    logger.info(f"服务端地址: {ZMQ_SERVER_IP}:{ZMQ_SERVER_PORT}")
+    logger.info(f"发包: {PKG_SEND_SHAPE}({SEND_INTERVAL*1000:.0f}ms) | 回包: {PKG_RECV_SHAPE}({RECV_INTERVAL*1000:.0f}ms)")
+    logger.info(f"预热规则: {PREHEAT_SECONDS}秒 / {PREHEAT_SEND_PACKS} 包后开启统计")
+    logger.info(f"收发比例: 每 {PACK_RATIO} 个发包对应 1 个回包")
+    logger.info("=" * 70)
+
+    # 启动ZMQ收发线程
+    io_thread = threading.Thread(target=zmq_io_thread, daemon=True, name="ZMQ_IO_Thread")
+    io_thread.start()
+
+    # 启动可视化
+    fig, lines, axes = init_plot()
+    ani = FuncAnimation(
+        fig, update_plot,
+        fargs=(lines, axes),
+        interval=PLOT_REFRESH_INTERVAL,
+        blit=True,
+        cache_frame_data=False
+    )
+
+    try:
+        plt.show()
+    except KeyboardInterrupt:
+        logger.info("收到 Ctrl+C 中断信号，准备退出")
+    finally:
+        # 输出最终完整汇总报表
+        run_total = time.perf_counter() - stat["start_time"]
+        total_send = stat["total_send"]
+        total_recv = stat["total_recv"]
+        v_send = stat["valid_send"]
+        v_recv = stat["valid_recv"]
+        t_recv = stat["theo_recv"]
+
+        loss_cnt = t_recv - v_recv
+        loss_rate = (loss_cnt / t_recv * 100) if t_recv > 0 else 0.0
+
+        logger.info(f"\n{'='*50} 最终运行汇总 {'='*50}")
+        logger.info(f"总运行时长: {run_total:.1f} s")
+        logger.info(f"【全局总包数】发送: {total_send} | 接收: {total_recv}")
+        logger.info(f"【有效统计区间(跳过预热{PREHEAT_SEND_PACKS}包)】")
+        logger.info(f"  有效发包: {v_send} | 理论应收包: {t_recv} | 实际收包: {v_recv}")
+        logger.info(f"  总丢包数: {loss_cnt} | 整体丢包率: {loss_rate:.2f} %")
+        logger.info(f"{'='*106}")
+
+        clean_resource()
+        sys.exit(0)
+
+if __name__ == "__main__":
+    main()

logs/log.py

@@ -1,87 +1,122 @@
-# log.py
 import os
-from datetime import datetime
+from pathlib import Path
+from datetime import datetime, timedelta
 import logging
 from logging.handlers import RotatingFileHandler
+import inspect
 from PubLibrary.InifileHelper import IniRead
 
 
+# 全局配置
 console_output = IniRead('system', 'console_output', '1')
 log_level = IniRead('system', 'algo_log_level', 'INFO')
-
-# 新增：日志去重缓存，key为日志内容，value为是否已打印
 log_once_cache = set()
+logger_cache = {}
+LOG_RETENTION_DAYS = 3
+
+LOG_PATH_STR = IniRead('system', 'algo_log_path', "d:/Program Files/64chn_Decoder/logs")
+LOG_DIR = Path(LOG_PATH_STR)
+# 自动补全路径分隔符，创建目录（不存在则新建，避免写日志报错）
+LOG_DIR.mkdir(parents=True, exist_ok=True)
+# 如需字符串格式路径
+LOG_DIR_STR = str(LOG_DIR) + "\\"
+LOG_FILE_PREFIX = 'algo_log_'
+
+# 日志格式：时间 - 日志器名 - 级别 - 文件名:行号 - 函数名 - 日志内容
+LOG_FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+DATE_FORMAT = '%Y-%m-%d %H:%M:%S'
 
 
-def init_module_logger():
-    """
-    初始化指定模块的日志器
-    :return: 对应模块的logger实例
-    """
-    # 缓存命中则直接返回
-    log_dir = './logs/'    # 确保日志目录存在
-    os.makedirs(log_dir, exist_ok=True)
+def clean_old_logs():
+    """清理超过指定天数的旧日志文件"""
+    try:
+        if not os.path.exists(LOG_DIR):
+            return
+        expire_date = datetime.now() - timedelta(days=LOG_RETENTION_DAYS)
+        for filename in os.listdir(LOG_DIR):
+            if not filename.startswith(LOG_FILE_PREFIX) or not filename.endswith('.log'):
+                continue
+            date_str = filename[len(LOG_FILE_PREFIX):-4]
+            try:
+                file_date = datetime.strptime(date_str, '%Y-%m-%d')
+                if file_date < expire_date:
+                    file_path = os.path.join(LOG_DIR, filename)
+                    os.remove(file_path)
+                    print(f"清理过期日志: {file_path}")
+            except ValueError:
+                continue
+    except Exception as e:
+        print(f"清理旧日志异常: {str(e)}")
 
-    log_file = os.path.join(log_dir, f'algo_log_{datetime.now().strftime("%Y-%m-%d")}.log')
 
-    # 初始化logger
-    logger = logging.getLogger('decoderLogger')
+def init_module_logger(logger_name):
+    """初始化日志器 + 清理旧日志"""
+    os.makedirs(LOG_DIR, exist_ok=True)
+    clean_old_logs()
+
+    current_date = datetime.now().strftime("%Y-%m-%d")
+    log_file = os.path.join(LOG_DIR, f"{LOG_FILE_PREFIX}{current_date}.log")
+
+    if logger_name in logger_cache:
+        return logger_cache[logger_name]
+
+    logger = logging.getLogger(logger_name)
     logger.setLevel(log_level)
-
     if logger.handlers:
+        logger_cache[logger_name] = logger
         return logger
 
-    # 设置日志轮转，最大10个文件，每个10MB
+    # 文件输出处理器
     file_handler = RotatingFileHandler(
         log_file,
         maxBytes=10 * 1024 * 1024,
         backupCount=10,
         encoding='utf-8'
     )
-    
-    # 日志格式
-    formatter = logging.Formatter(
-        '%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S'
-    )
+    formatter = logging.Formatter(LOG_FORMAT, datefmt=DATE_FORMAT)
     file_handler.setFormatter(formatter)
-    logger.setLevel(log_level)
     logger.addHandler(file_handler)
 
+    # 控制台输出
     if console_output:
         console_handler = logging.StreamHandler()
         console_handler.setFormatter(formatter)
         logger.addHandler(console_handler)
 
+    logger_cache[logger_name] = logger
     return logger
 
+
 def algo_log(content, level="INFO", record_once=False):
     """
-    通用日志函数，支持按模块输出到不同日志文件
-    :param content: 日志内容
-    :param level: 日志级别（DEBUG/INFO/WARNING/ERROR/FATAL）
-    :param record_once: 是否只打印一次该日志内容，默认False
+    日志入口函数
+    自动记录：调用文件名、代码行号、所在函数
     """
-    # 初始化模块日志器
-    logger = init_module_logger()
+    # 回溯栈帧，获取真正调用 algo_log 的代码位置
+    # f_back(1) -> algo_log 自身，f_back(2) -> 业务调用处
+    frame = inspect.currentframe().f_back.f_back
+    if not frame:
+        file_name = "unknown"
+    else:
+        file_name = os.path.basename(frame.f_code.co_filename)
 
-    # 新增：处理只打印一次的逻辑
+    logger = init_module_logger(file_name)
+
+    # 单次日志去重
     if record_once:
-        # 生成唯一标识（可根据需要调整，比如拼接level增强唯一性）
         log_key = f"{level.upper()}_{content}"
         if log_key in log_once_cache:
-            return  # 已打印过，直接返回
-        log_once_cache.add(log_key)  # 未打印过，加入缓存
+            return
+        log_once_cache.add(log_key)
 
-    # 根据级别输出日志
+    # 日志级别分发
     level_upper = level.upper()
-    if level_upper == "DEBUG":
-        logger.debug(content)
-    elif level_upper == "WARNING":
-        logger.warning(content)
-    elif level_upper == "ERROR":
-        logger.error(content)
-    elif level_upper == "FATAL":
-        logger.fatal(content)
-    else:  # 默认INFO级别
-        logger.info(content)
+    log_map = {
+        "DEBUG": logger.debug,
+        "WARNING": logger.warning,
+        "ERROR": logger.error,
+        "FATAL": logger.fatal,
+        "INFO": logger.info
+    }
+    log_func = log_map.get(level_upper, logger.info)
+    log_func(content)

nuitka_3in1_package.sh

@@ -0,0 +1,55 @@
+#!/bin/bash
+# Git Bash 中文 UTF-8 兼容配置（通用版，无报错）
+export LC_ALL=en_US.UTF-8
+export LANG=en_US.UTF-8
+
+echo "========================"
+echo "Nuitka 打包脚本 - 优化稳定版"
+echo "适配：PyTorch2.0.0 + CUDA11.7 + 脑电解码项目"
+echo "========================"
+
+# ===================== 自定义配置区 =====================
+PY_FILE="runDecoder.py"       # 主程序文件
+OUT_DIR="dist_nuitka"         # 输出文件夹
+MODEL_DIR="online_Models"      # 模型文件夹
+# ========================================================
+
+# 检查主文件是否存在
+if [ ! -f "${PY_FILE}" ]; then
+    echo "错误：未找到主文件 ${PY_FILE}，请检查路径！"
+    read -n 1 -s -r -p "按任意键退出"
+    exit 1
+fi
+
+echo "开始打包：${PY_FILE}"
+echo "输出目录：${OUT_DIR}"
+
+# Nuitka 核心打包命令（无错误、无冗余、全依赖）
+python -m nuitka \
+--standalone \
+--msvc=latest \
+--windows-console-mode=force \
+--module-parameter=torch-disable-jit=yes \
+--enable-plugin=no-qt \
+--include-package=numpy \
+--include-module=numpy.core._multiarray_umath \
+--include-package=scipy \
+--no-deployment-flag=self-execution \
+--include-data-dir="${MODEL_DIR}=${MODEL_DIR}" \
+--output-dir="${OUT_DIR}" \
+--remove-output \
+"${PY_FILE}"
+
+# 打包结果判断
+if [ $? -eq 0 ]; then
+    echo -e "\n========================"
+    echo "✅ 打包成功！"
+    echo "📦 产物路径：${OUT_DIR}/${PY_FILE%.py}.exe"
+    echo "========================"
+else
+    echo -e "\n❌ 打包失败！"
+fi
+
+# Git Bash 兼容的暂停
+read -n 1 -s -r -p "按任意键退出..."
+echo

online_Models/log_result.txt

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-244    0.5625
-245    0.375
-246    0.375
-247    0.375
-248    0.3125
-249    0.375
-The average accuracy is: 0.42675
-The best accuracy is: 0.5625

requirements.txt

@@ -0,0 +1,52 @@
+Bottleneck==1.4.2
+brotlicffi==1.2.0.0
+certifi==2026.5.20
+cffi==2.0.0
+charset-normalizer==3.4.4
+contourpy==1.3.2
+cycler==0.12.1
+einops==0.8.2
+filelock==3.20.3
+fonttools==4.63.0
+gmpy2==2.2.2
+idna==3.11
+Jinja2==3.1.6
+joblib==1.5.3
+kiwisolver==1.5.0
+MarkupSafe==3.0.2
+matplotlib==3.10.9
+mkl_fft==1.3.11
+mkl_random==1.2.8
+mkl-service==2.5.2
+mpmath==1.3.0
+networkx==3.4.2
+Nuitka==4.1.1
+numexpr==2.14.1
+numpy==1.24.3
+packaging==26.0
+pandas==2.3.3
+pillow==12.2.0
+pip==26.0.1
+pycparser==3.0
+pyparsing==3.3.2
+pyserial==3.5
+PySocks==1.7.1
+python-dateutil==2.9.0.post0
+pytz==2026.1.post1
+pyzmq==27.1.0
+requests==2.33.1
+scikit-learn==1.7.1
+scipy==1.15.3
+setuptools==82.0.1
+six==1.17.0
+sympy==1.14.0
+threadpoolctl==3.5.0
+torch==2.0.0
+torchaudio==2.0.0
+torchsummary==1.5.1
+torchvision==0.15.0
+typing_extensions==4.15.0
+tzdata==2026.2
+urllib3==2.7.0
+wheel==0.46.3
+win_inet_pton==1.1.0

runDecoder.py

@@ -1,37 +1,38 @@
-import matplotlib
-matplotlib.use('Agg')
-import argparse
-import sys
+# import matplotlib
+# matplotlib.use('Agg')
+# import argparse
+# import sys
 import time
 from Decoder import Decoder_main
 from PubLibrary.RunOnce import is_program_running
 from PubLibrary.InifileHelper import IniRead
+from logs.log import algo_log
 
 def get_device_info(device_type):
     
-    
     section = f'device_type_{device_type}'
     device_info = {
-        'device_sample_rate': int(IniRead(section, 'sample_rate')) if IniRead(section, 'sample_rate') is not None else 250,
-        
-        ''
+        'sample_rate': int(IniRead(section, 'sample_rate')) if IniRead(section, 'sample_rate') is not None else 250,
+        'frame_points': int(IniRead(section, 'frame_points')) if IniRead(section, 'frame_points') is not None else 5,
+        'channel_nums': int(IniRead(section, 'channel_nums')) if IniRead(section, 'channel_nums') is not None else 66,
+        'channel_names': IniRead(section, 'channel_names') if IniRead(section, 'channel_names') is not None else None,
+        'channel_index': IniRead(section, 'channel_index') if IniRead(section, 'channel_index') is not None else None, 
     }
     
+    return device_info
+
+
 if __name__ == "__main__":
     if not is_program_running():
         # 解析命令行参数
-        parser = argparse.ArgumentParser(description="EEG Decoder Application")
-        parser.add_argument('-dt', '-t','--device-type', type=int, default=None, help="Device Type")
+        # parser = argparse.ArgumentParser(description="EEG Decoder Application")
+        # parser.add_argument('-dt', '-t','--device-type', type=int, default=None, help="Device Type")
         # parser.add_argument('-dh', '--device-host', type=str, default=None, help="Device Host IP")
         # parser.add_argument('-dp', '--device-port', type=int, default=None, help="Device Port")
         # parser.add_argument('-uh', '--upper-host', type=str, default=None, help="Upper Computer Host IP")
         # parser.add_argument('-up', '--upper-port', type=int, default=None, help="Upper Computer Port")
+        # args = parser.parse_args()
 
-        args = parser.parse_args()
-        device_info= get_device_info(args.device_type)
-        
-
-        decoder = Decoder_main(device_info=device_info)
         # decoder.connect(
         #     device_type=args.device_type,
         #     device_host=args.device_host,
@@ -40,6 +41,10 @@ if __name__ == "__main__":
         #     upper_port=args.upper_port
         # )
 
+        device_info= get_device_info(1)
+        algo_log(f"device_info: {device_info}", level="DEBUG")
+        decoder = Decoder_main(device_info=device_info)
+
         try:
             decoder.start()
             while not decoder.zmqServer.IsExitApp:

upperHost_stimmock/MI_headless.py

@@ -0,0 +1,306 @@
+"""
+MI_headless.py
+无界面版 MI 运动想象范式通讯流程模拟脚本。
+复现 MI_main.py 的完整指令序列（train 0/1, rest, predict, saveData），
+但不依赖 psychopy 也不打开任何窗口/音频，用 time.sleep 替代帧循环等待。
+
+启动顺序:
+    1. runDecoder.py
+    2. datamock.py
+    3. MI_headless.py
+"""
+
+import sys
+import os
+import json
+import time
+import threading
+import zmq
+import numpy as np
+import ast
+from datetime import datetime
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from PubLibrary.InifileHelper import IniRead
+
+personname = 'demo'
+session = '01'
+
+DATAMOCK_LABEL_ADDR = 'tcp://127.0.0.1:8101'  # datamock 标签命令地址
+
+
+# ========== ZMQ 结果接收服务 ==========
+class ZmqResultServer(threading.Thread):
+    def __init__(self, port=8088):
+        threading.Thread.__init__(self)
+        self.port = port
+        self.running = True
+        self.energy = 0
+        self.paradigm = 0   # 0=个体校准, 1=康复训练, 2=等待模型训练
+        self.ChoosenNum = -1
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.ROUTER)
+        self.socket.bind(f"tcp://0.0.0.0:{self.port}")
+        self.daemon = True
+        self.trial_idx = 0
+
+    def run(self):
+        print(f"[Server] UpperHost_Server listening on {self.port}")
+        while self.running:
+            try:
+                frames = self.socket.recv_multipart(zmq.NOBLOCK)
+                if len(frames) < 3:
+                    continue
+                message = json.loads(frames[2].decode('utf-8'))
+                method = message.get('method')
+                params = message.get('params')
+                if method == 'energy':
+                    self.energy = params
+                elif method == 'paradigm':
+                    self.paradigm = params
+                    print(f"[Server] paradigm -> {params}")
+                elif method == 'result':
+                    self.ChoosenNum = params
+                    self.trial_idx += 1
+                    print(f"[Server] result={self.ChoosenNum}  (trial {self.trial_idx})")
+            except zmq.Again:
+                time.sleep(0.005)
+            except Exception as e:
+                print(f"[Server] error: {e}")
+
+    def stop(self):
+        self.running = False
+        self.socket.close()
+        self.context.term()
+
+
+# ========== ZMQ 命令发送客户端 ==========
+class ZmqCmdClient:
+    def __init__(self, host, port):
+        self.host = host
+        self.port = port
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.DEALER)
+        # PUSH socket 用于向 datamock.py 发送标签命令
+        self._label_sock = self.context.socket(zmq.PUSH)
+        self._label_sock.connect(DATAMOCK_LABEL_ADDR)
+        print(f"[Client] label PUSH connected to {DATAMOCK_LABEL_ADDR}")
+
+    def connect(self):
+        self.socket.connect(f"tcp://{self.host}:{self.port}")
+        print(f"[Client] connected to {self.host}:{self.port}")
+
+    def start_recv_thread(self, result_server):
+        """启动后台线程，持续接收 decoder 通过 8099 ROUTER 回发的消息，并更新 result_server 的状态"""
+        self._result_server = result_server
+        self._stop_recv = threading.Event()
+
+        def _recv_loop():
+            while not self._stop_recv.is_set():
+                try:
+                    frames = self.socket.recv_multipart(zmq.NOBLOCK)
+                    # DEALER 收到的格式: [b'', json_bytes]
+                    data_bytes = frames[-1]
+                    message = json.loads(data_bytes.decode('utf-8'))
+                    method = message.get('method')
+                    params = message.get('params')
+                    ts = datetime.now().strftime('%H:%M:%S.%f')[:-3]
+                    print(f"[{ts}] [CmdClient] recv: {method}={params}")
+                    if method == 'paradigm':
+                        self._result_server.paradigm = params
+                        print(f"[{ts}] [CmdClient] paradigm updated -> {params}")
+                    elif method == 'result':
+                        self._result_server.ChoosenNum = params
+                        self._result_server.trial_idx += 1
+                        print(f"[{ts}] [CmdClient] result={params} (trial {self._result_server.trial_idx})")
+                    elif method == 'energy':
+                        self._result_server.energy = params
+                except zmq.Again:
+                    time.sleep(0.005)
+                except Exception as e:
+                    print(f"[CmdClient recv] error: {e}")
+                    time.sleep(0.01)
+
+        self._recv_thread = threading.Thread(target=_recv_loop, daemon=True)
+        self._recv_thread.start()
+        print(f"[Client] 后台接收线程已启动（监听 decoder 8099 回发消息）")
+
+    def stop_recv_thread(self):
+        if hasattr(self, '_stop_recv'):
+            self._stop_recv.set()
+
+    def _send_label(self, label_value):
+        """向 datamock.py 发送标签命令"""
+        try:
+            self._label_sock.send_string(str(label_value), zmq.NOBLOCK)
+        except Exception as e:
+            print(f"[Client] label send error: {e}")
+
+    def send_data(self, method, params):
+        msg = {'method': method, 'params': params}
+        try:
+            self.socket.send_multipart([b'', json.dumps(msg).encode('utf-8')])
+            ts = datetime.now().strftime('%H:%M:%S.%f')[:-3]
+            print(f"[{ts}] send_data: {method}={params}")
+            # 根据 train/predict 命令向 datamock 发送标签
+            if method == 'train':
+                if params == 0:
+                    self._send_label(1)
+                    print(f"[Label] train 0 -> datamock label=1")
+                elif params == 1:
+                    self._send_label(2)
+                    print(f"[Label] train 1 -> datamock label=2")
+            elif method == 'predict':
+                self._send_label(99)
+                print(f"[Label] predict -> datamock label=99")
+        except Exception as e:
+            print(f"[Client] send error: {e}")
+
+
+# ========== 主流程 ==========
+def run_headless():
+    server = ZmqResultServer(port=8088)
+    server.start()
+
+    _dh = str(IniRead('system', 'Decoder_Host'))
+    _dp = int(IniRead('system', 'Decoder_Port'))
+    client = ZmqCmdClient(_dh, _dp)
+    client.connect()
+    client.start_recv_thread(server)  # 启动后台接收线程，监听 decoder 8099 回发的 paradigm/result 消息
+
+    time.sleep(1)  # 等待连接建立
+    client.send_data('decoderClass', 'mi')
+    time.sleep(4)  # 等待 zmqServer 排空启动积压包（datamock 提前连接会积压 ~3s 数据）
+
+    # MI_IntervalEpoch = [0.5, 4.5]，trial时长 = 4.5-0.5 = 4.0s
+    _mi_iv = ast.literal_eval(IniRead('system', 'MI_IntervalEpoch'))  # [0.5, 4.5]
+    _trial_sec = float(_mi_iv[1] - _mi_iv[0])  # 4.0s
+    _margin = 1.0
+    train_time = max(5.0, _trial_sec + _margin)  # 训练刺激时长（与 MI_main.py 保持一致）
+
+    # MI epoch latency = interval_epoch[1] // 5 = (4.5*250)//5 = 225包 × 20ms = 4.5s
+    # train_latency = 225包（MI中 train_latency == latency）
+    # 在 train_time 后需再等 epoch_wait 秒，decoder 才能完成 epoch 采集
+    epoch_wait = _mi_iv[1] / _mi_iv[1] * (_mi_iv[1] * 250 // 5) * 0.02  # = latency * 20ms
+    # 更直接的计算：latency = interval_epoch[1] // 5 = int(4.5*250)//5 = 225，225*0.02 = 4.5s
+    epoch_wait = (int(_mi_iv[1] * 250) // 5) * 0.02  # 4.5s
+
+    # predict epoch wait（与 train 相同，MI中 latency == train_latency）
+    predict_epoch_wait = epoch_wait  # 4.5s
+
+    test_time = 7.0   # 预测窗口时长（与 MI_main.py 保持一致）
+    right_rehabilitation = float(IniRead('system', 'Right_rehabilitation'))
+    fault_rehabilitation = float(IniRead('system', 'Fault_rehabilitation'))
+    rest_time = float(IniRead('system', 'Rest_time'))
+
+    num_blocks = int(IniRead('system', 'Num_blocks'))
+    num_trials = int(IniRead('system', 'Num_trials'))
+
+    trained = 0
+    Num_Total = 0
+    Num_Success = 0
+    user_choice = []
+
+    print("=" * 50)
+    print("[Headless] 开始运行 MI 通讯流程（无界面）")
+    print(f"  MI_IntervalEpoch={_mi_iv}, trial_sec={_trial_sec:.2f}s")
+    print(f"  train_time={train_time:.2f}s, epoch_wait={epoch_wait:.2f}s")
+    print(f"  test_time={test_time:.2f}s, predict_epoch_wait={predict_epoch_wait:.2f}s")
+    print(f"  num_blocks={num_blocks}, num_trials={num_trials}")
+    print("=" * 50)
+
+    try:
+        while True:
+            # -------- 个体校准阶段 --------
+            print("\n[Phase] 个体校准阶段 (paradigm=0)")
+            client.send_data('rest', 0)
+            time.sleep(1)
+
+            while server.paradigm == 0:
+                # 左侧 MI 刺激（train 0，label=1）
+                print(f"\n[Train] 左侧 MI 刺激 (train 0) trained={trained}")
+                client.send_data('rest', 0)
+                time.sleep(0.5)  # ding 提示后等待
+
+                client.send_data('train', 0)
+                time.sleep(train_time + 0.2)  # 等待刺激时间 + epoch 完成时间
+
+                trained += 1
+                client.send_data('rest', 0)
+                time.sleep(1.0)  # 类间休息
+
+                # 空闲态样本采集（train 1，label=2）
+                print(f"\n[Train] 空闲态采集 (train 1) trained={trained}")
+                client.send_data('train', 1)
+                time.sleep(train_time + 0.2)   # 等待刺激时间 + epoch 完成时间
+
+                trained += 1
+                client.send_data('rest', 0)
+                time.sleep(1.0)  # 类间休息
+
+            # 个体校准阶段结束
+            print("\n[Phase] 个体校准结束，等待模型训练 (paradigm=2) ...")
+            trained = 0
+            time.sleep(1)
+
+            # 等待模型训练完成 (paradigm=2 -> paradigm=1)
+            while server.paradigm == 2:
+                print("[Phase] 等待模型训练完成 ...")
+                time.sleep(0.5)
+
+            # -------- 康复训练阶段 --------
+            while server.paradigm == 1:
+                print("\n[Phase] 康复训练阶段 (paradigm=1)")
+                for block_idx in range(num_blocks):
+                    print(f"\n  [Block {block_idx+1}/{num_blocks}]")
+                    time.sleep(10)  # 每轮开始前等待
+
+                    for trial_idx in range(num_trials):
+                        print(f"  [Trial {trial_idx+1}/{num_trials}]")
+
+                        time.sleep(0.5)  # ding 提示
+                        server.ChoosenNum = -1
+
+                        # 开始预测
+                        # MI predict epoch latency = 225包 × 20ms = 4.5s，需额外等待 epoch 完成
+                        client.send_data('predict', 1)
+                        t_start = time.perf_counter()
+                        while time.perf_counter() - t_start < test_time + predict_epoch_wait:
+                            if server.ChoosenNum >= 0:
+                                Num_Total += 1
+                                user_choice.append(server.ChoosenNum)
+                                if server.ChoosenNum == 0:
+                                    Num_Success += 1
+                                    rest_time = right_rehabilitation
+                                elif server.ChoosenNum == 1:
+                                    rest_time = fault_rehabilitation
+                                break
+                            time.sleep(0.02)
+
+                        trained += 1
+                        client.send_data('rest', 0)
+                        time.sleep(0.5)
+                        time.sleep(rest_time)
+                        server.ChoosenNum = -1
+
+                # 训练结束
+                print("\n[Phase] 康复训练结束")
+                break  # 退出康复训练循环
+
+            # 统计结果
+            overall_accuracy = Num_Success / Num_Total if Num_Total > 0 else 0
+            print(f"\n[Result] Overall={overall_accuracy:.3f} ({Num_Success}/{Num_Total})")
+            print(f"[Result] user_choice={user_choice}")
+            break  # 完成一个完整流程后退出
+
+    except KeyboardInterrupt:
+        print("\n[Headless] 用户中断")
+    finally:
+        client.send_data('predict', 2)   # 关闭系统
+        client.send_data('saveData', 0)
+        server.stop()
+        print("[Headless] 已发送关闭指令，退出。")
+
+
+if __name__ == '__main__':
+    run_headless()

upperHost_stimmock/ssmvep_headless.py

@@ -0,0 +1,301 @@
+"""
+ssmvep_headless.py
+无界面版 SSMVEP 范式通讯流程模拟脚本。
+复现 ssmvep_main.py 的完整指令序列（train 0/1/2, rest, predict, saveData），
+但不依赖 psychopy 也不打开任何窗口/音频，用 time.sleep 替代帧循环等待。
+
+启动顺序:
+    1. runDecoder.py
+    2. datamock.py
+    3. ssmvep_headless.py
+"""
+
+import sys
+import os
+import json
+import time
+import threading
+import zmq
+import numpy as np
+from datetime import datetime
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from PubLibrary.InifileHelper import IniRead
+
+personname = 'demo'
+session = '01'
+
+DATAMOCK_LABEL_ADDR = 'tcp://127.0.0.1:8101'  # datamock 标签命令地址
+
+
+# ========== ZMQ 结果接收服务 ==========
+class ZmqResultServer(threading.Thread):
+    def __init__(self, port=8088):
+        threading.Thread.__init__(self)
+        self.port = port
+        self.running = True
+        self.energy = 0
+        self.paradigm = 0   # 0=个体校准, 1=康复训练, 2=等待模型训练
+        self.ChoosenNum = -1
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.ROUTER)
+        self.socket.bind(f"tcp://0.0.0.0:{self.port}")
+        self.daemon = True
+        self.trial_idx = 0
+
+    def run(self):
+        print(f"[Server] UpperHost_Server listening on {self.port}")
+        while self.running:
+            try:
+                frames = self.socket.recv_multipart(zmq.NOBLOCK)
+                if len(frames) < 3:
+                    continue
+                message = json.loads(frames[2].decode('utf-8'))
+                method = message.get('method')
+                params = message.get('params')
+                if method == 'energy':
+                    self.energy = params
+                elif method == 'paradigm':
+                    self.paradigm = params
+                    print(f"[Server] paradigm -> {params}")
+                elif method == 'result':
+                    self.ChoosenNum = params
+                    self.trial_idx += 1
+                    print(f"[Server] result={self.ChoosenNum}  (trial {self.trial_idx})")
+            except zmq.Again:
+                time.sleep(0.005)
+            except Exception as e:
+                print(f"[Server] error: {e}")
+
+    def stop(self):
+        self.running = False
+        self.socket.close()
+        self.context.term()
+
+
+# ========== ZMQ 命令发送客户端 ==========
+class ZmqCmdClient:
+    def __init__(self, host, port):
+        self.host = host
+        self.port = port
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.DEALER)
+        # PUSH socket 用于向 datamock.py 发送标签命令
+        self._label_sock = self.context.socket(zmq.PUSH)
+        self._label_sock.connect(DATAMOCK_LABEL_ADDR)
+        print(f"[Client] label PUSH connected to {DATAMOCK_LABEL_ADDR}")
+
+    def connect(self):
+        self.socket.connect(f"tcp://{self.host}:{self.port}")
+        print(f"[Client] connected to {self.host}:{self.port}")
+
+    def start_recv_thread(self, result_server):
+        """启动后台线程，持续接收 decoder 通过 8099 ROUTER 回发的消息，并更新 result_server 的状态"""
+        self._result_server = result_server
+        self._stop_recv = threading.Event()
+
+        def _recv_loop():
+            while not self._stop_recv.is_set():
+                try:
+                    frames = self.socket.recv_multipart(zmq.NOBLOCK)
+                    # DEALER 收到的格式: [b'', json_bytes]
+                    data_bytes = frames[-1]
+                    message = json.loads(data_bytes.decode('utf-8'))
+                    method = message.get('method')
+                    params = message.get('params')
+                    ts = datetime.now().strftime('%H:%M:%S.%f')[:-3]
+                    print(f"[{ts}] [CmdClient] recv: {method}={params}")
+                    if method == 'paradigm':
+                        self._result_server.paradigm = params
+                        print(f"[{ts}] [CmdClient] paradigm updated -> {params}")
+                    elif method == 'result':
+                        self._result_server.ChoosenNum = params
+                        self._result_server.trial_idx += 1
+                        print(f"[{ts}] [CmdClient] result={params} (trial {self._result_server.trial_idx})")
+                    elif method == 'energy':
+                        self._result_server.energy = params
+                except zmq.Again:
+                    time.sleep(0.005)
+                except Exception as e:
+                    print(f"[CmdClient recv] error: {e}")
+                    time.sleep(0.01)
+
+        self._recv_thread = threading.Thread(target=_recv_loop, daemon=True)
+        self._recv_thread.start()
+        print(f"[Client] 后台接收线程已启动（监听 decoder 8099 回发消息）")
+
+    def stop_recv_thread(self):
+        if hasattr(self, '_stop_recv'):
+            self._stop_recv.set()
+
+    def _send_label(self, label_value):
+        """向 datamock.py 发送标签命令"""
+        try:
+            self._label_sock.send_string(str(label_value), zmq.NOBLOCK)
+        except Exception as e:
+            print(f"[Client] label send error: {e}")
+
+    def send_data(self, method, params):
+        msg = {'method': method, 'params': params}
+        try:
+            self.socket.send_multipart([b'', json.dumps(msg).encode('utf-8')])
+            ts = datetime.now().strftime('%H:%M:%S.%f')[:-3]
+            print(f"[{ts}] send_data: {method}={params}")
+            # 根据 train/predict 命令向 datamock 发送标签
+            if method == 'train':
+                if params == 0:
+                    self._send_label(1)
+                    print(f"[Label] train 0 -> datamock label=1")
+                elif params == 1:
+                    self._send_label(2)
+                    print(f"[Label] train 1 -> datamock label=2")
+            elif method == 'predict':
+                self._send_label(99)
+                print(f"[Label] predict -> datamock label=99")
+        except Exception as e:
+            print(f"[Client] send error: {e}")
+
+
+# ========== 主流程 ==========
+def run_headless():
+    server = ZmqResultServer(port=8088)
+    server.start()
+
+    _dh = str(IniRead('system', 'Decoder_Host'))
+    _dp = int(IniRead('system', 'Decoder_Port'))
+    client = ZmqCmdClient(_dh, _dp)
+    client.connect()
+    client.start_recv_thread(server)  # 启动后台接收线程，监听 decoder 8099 回发的 paradigm/result 消息
+
+    time.sleep(1)  # 等待连接建立
+    client.send_data('decoderClass', 'ssmvep')
+
+    train_time   = 2.5   # 每轮训练刺激时长 (s)
+    test_time    = 2.5   # 每轮测试刺激时长 (s)
+    right_rehabilitation = float(IniRead('system', 'Right_rehabilitation'))
+    fault_rehabilitation = float(IniRead('system', 'Fault_rehabilitation'))
+    rest_time    = float(IniRead('system', 'Rest_time'))
+
+    num_blocks = int(IniRead('system', 'Num_blocks'))
+    num_trials = int(IniRead('system', 'Num_trials'))
+
+    position = [0, 1]
+    truePos_seq = position * int(num_trials / len(position))
+    truePos_seq = np.random.permutation(truePos_seq).tolist()
+    user_choice = []
+
+    os.makedirs('EEGFiles', exist_ok=True)
+    seq_file_path = f'EEGFiles/pos_seq_{personname}{session}_{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}.json'
+    seq_info = {
+        'position': position,
+        'sequence': truePos_seq,
+        'start_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    }
+    with open(seq_file_path, 'w', encoding='utf-8') as f:
+        json.dump(seq_info, f, ensure_ascii=False, indent=2)
+
+    trained = 0
+    Num_Total = 0
+    Num_Success = 0
+
+    print("=" * 50)
+    print("[Headless] 开始运行 SSMVEP 通讯流程（无界面）")
+    print(f"  num_blocks={num_blocks}, num_trials={num_trials}")
+    print(f"  train_time={train_time}s, test_time={test_time}s")
+    print("=" * 50)
+
+    try:
+        while True:
+            # -------- 个体校准阶段 --------
+            print("\n[Phase] 个体校准阶段 (paradigm=0)")
+            client.send_data('rest', 0)
+            time.sleep(1)
+
+            # epoch完成需要的额外等待时间：train_latency=120包×20ms=2.4s
+            # 在train_time后需再等epoch_wait秒，decoder才能完成epoch采集并取出数据
+            epoch_wait = 2.4  # 秒，与train_latency对应
+
+            while server.paradigm == 0:
+                # 左腿刺激
+                print(f"\n[Train] 左腿刺激 (train 0) trained={trained}")
+                client.send_data('train', 0)
+                time.sleep(train_time + epoch_wait)  # 等待刺激时间+epoch完成时间
+
+                trained += 1
+                client.send_data('rest', 0)
+                time.sleep(max(0, abs(fault_rehabilitation - train_time) - epoch_wait))
+
+                # 右腿刺激
+                print(f"\n[Train] 右腿刺激 (train 1) trained={trained}")
+                client.send_data('train', 1)
+                time.sleep(train_time + epoch_wait)  # 等待刺激时间+epoch完成时间
+
+                trained += 1
+                client.send_data('rest', 0)
+                time.sleep(max(0, fault_rehabilitation - epoch_wait))
+
+            # 个体校准阶段结束
+            print("\n[Phase] 个体校准结束，等待 paradigm=1 ...")
+            trained = 0
+            time.sleep(1)
+
+            # -------- 康复训练阶段 --------
+            while server.paradigm == 1:
+                print("\n[Phase] 康复训练阶段 (paradigm=1)")
+                for block_idx in range(num_blocks):
+                    print(f"\n  [Block {block_idx+1}/{num_blocks}]")
+                    time.sleep(10)  # 每轮开始前等待
+
+                    for trial_idx in range(num_trials):
+                        true_position = truePos_seq[trial_idx]
+                        print(f"  [Trial {trial_idx+1}/{num_trials}] true_pos={true_position}")
+
+                        time.sleep(0.5)  # 提示 + 叮声
+                        server.ChoosenNum = -1
+
+                        # 开始测试
+                        # predict epoch latency = 115包×20ms = 2.3s，需额外等待epoch完成
+                        predict_epoch_wait = 2.3  # 秒，与predict latency=115包对应
+                        client.send_data('predict', 1)
+                        t_start = time.perf_counter()
+                        while time.perf_counter() - t_start < test_time + predict_epoch_wait:
+                            if server.ChoosenNum >= 0:
+                                Num_Total += 1
+                                user_choice.append(server.ChoosenNum)
+                                if server.ChoosenNum in [0, 1]:
+                                    Num_Success += 1
+                                    rest_time = right_rehabilitation
+                                break
+                            time.sleep(0.02)
+
+                        trained += 1
+                        client.send_data('rest', 0)
+                        time.sleep(0.5)
+                        time.sleep(rest_time)
+                        server.ChoosenNum = -1
+
+                # 训练结束
+                print("\n[Phase] 康复训练结束")
+                break  # 退出康复训练循环
+
+            # 统计结果
+            overall_accuracy = Num_Success / Num_Total if Num_Total > 0 else 0
+            expected_seq = truePos_seq * num_blocks
+            min_len = min(len(user_choice), len(expected_seq))
+            same_count = sum(1 for a, b in zip(user_choice[:min_len], expected_seq[:min_len]) if a == b)
+            true_accuracy = same_count / min_len if min_len > 0 else 0
+            print(f"\n[Result] Overall={overall_accuracy:.3f} ({Num_Success}/{Num_Total})")
+            print(f"[Result] TrueAcc={true_accuracy:.3f} ({same_count}/{min_len})")
+            break  # 完成一个完整流程后退出
+
+    except KeyboardInterrupt:
+        print("\n[Headless] 用户中断")
+    finally:
+        client.send_data('predict', 2)   # 关闭系统
+        client.send_data('saveData', 0)
+        server.stop()
+        print("[Headless] 已发送关闭指令，退出。")
+
+
+if __name__ == '__main__':
+    run_headless()

upperHost_stimmock/ssvep_main.py

@@ -0,0 +1,364 @@
+import time
+
+from psychopy import visual, core, logging  # import some libraries from PsychoPy
+import random
+from datetime import datetime
+
+# LAB STREAMING LAYER1
+from pylsl import StreamInfo, StreamOutlet
+from psychopy import event
+import numpy as np
+from DecoderDW.Server import TCPServer
+from DecoderDW.Client import TCPClient
+# import subprocess
+
+# ----------------------
+# constants
+# size of the window
+WINWIDTH = 1920
+WINHEIGHT = 1080
+REFRESH_RATE = 144
+
+
+
+def get_keypress():
+    keys = event.getKeys()
+    if keys:
+        return keys[0]
+    else:
+        return None
+
+
+def shutdown(win,client):
+    client.send_data('saveData', 0)
+    client.send_data('predict',2)
+    win.close()
+    core.quit()
+
+
+# end of configuration
+# ----------------------
+
+def generate_square_wave(frequency, sampling_rate=REFRESH_RATE, duration=5):
+    """
+    生成方波序列
+
+    参数：
+        frequency (float): 频率（Hz）
+        sampling_rate (int): 采样率（Hz），应与屏幕刷新率一致
+        duration (float): 时长（秒）
+
+    返回：
+        square_wave (list): 方波序列
+    """
+    # 计算总点数
+    n_points = int(duration * sampling_rate)
+
+    # 生成时间序列
+    time = np.linspace(0, duration, n_points, endpoint=False)
+
+    # 生成正弦波数据
+    sin_wave = np.sin(2 * np.pi * frequency * time)
+    # 生成方波数据
+    square_wave = np.where(sin_wave >= 0, 1, 0)
+
+    return square_wave.tolist()
+
+
+# 启动一个进程，不等待其完成
+import os
+if __name__ == "__main__":
+    # ----------------------------------------------------------------------------------
+    # main window settings
+    main_win = visual.Window(size=(WINWIDTH, WINHEIGHT), units='height', screen=0, fullscr=False,
+                             gammaErrorPolicy='warn', color=(0.7, 0.7, 0.7))
+    print('starting  1')
+    # Set up LabStreamingLayer stream.
+    info = StreamInfo(name='psychopy_stimuli', type='Markers', channel_count=1, channel_format='string',
+                      source_id='psychopy_stimuli_001')
+    outlet = StreamOutlet(info)  # Broadcast the stream.
+
+    imageStim1 = visual.ImageStim(main_win, size=(300, 300), pos=(-600, 300), units='pix', image='UI/figures/xy.jpg')
+    txtStim1 = visual.TextStim(win=main_win, text='△', font='SimHei', height=80, color='black', units='pix', bold=True,
+                               italic=False, pos=(-600, 30))
+
+    imageStim2 = visual.ImageStim(main_win, size=(300, 300), pos=(0, 300), units='pix', image='UI/figures/xy.jpg')
+    txtStim2 = visual.TextStim(win=main_win, text='△', font='SimHei', height=80, color='black', units='pix', bold=True,
+                               italic=False, pos=(0, 30))
+
+    imageStim3 = visual.ImageStim(main_win, size=(300, 300), pos=(600, 300), units='pix', image='UI/figures/xy.jpg')
+    txtStim3 = visual.TextStim(win=main_win, text='△', font='SimHei', height=80, color='black', units='pix', bold=True,
+                               italic=False, pos=(600, 30))
+    imageStim4 = visual.ImageStim(main_win, size=(300, 300), pos=(-600, -200), units='pix', image='UI/figures/xy.jpg')
+    txtStim4 = visual.TextStim(win=main_win, text='△', font='SimHei', height=80, color='black', units='pix', bold=True,
+                               italic=False, pos=(-600, -470))
+    imageStim5 = visual.ImageStim(main_win, size=(300, 300), pos=(0, -200), units='pix', image='UI/figures/xy.jpg')
+    txtStim5 = visual.TextStim(win=main_win, text='△', font='SimHei', height=80, color='black', units='pix', bold=True,
+                               italic=False, pos=(0, -470))
+    imageStim6 = visual.ImageStim(main_win, size=(300, 300), pos=(600, -200), units='pix', image='UI/figures/xy.jpg')
+    txtStim6 = visual.TextStim(win=main_win, text='△', font='SimHei', height=80, color='black', units='pix', bold=True,
+                               italic=False, pos=(600, -470))
+    imageStim1red = visual.ImageStim(main_win, size=(300, 300), pos=(-600, 300), units='pix', image='UI/figures/xy_red.jpg')
+    imageStim2red = visual.ImageStim(main_win, size=(300, 300), pos=(0, 300), units='pix', image='UI/figures/xy_red.jpg')
+    imageStim3red = visual.ImageStim(main_win, size=(300, 300), pos=(600, 300), units='pix', image='UI/figures/xy_red.jpg')
+    imageStim4red = visual.ImageStim(main_win, size=(300, 300), pos=(-600, -200), units='pix', image='UI/figures/xy_red.jpg')
+    imageStim5red = visual.ImageStim(main_win, size=(300, 300), pos=(0, -200), units='pix', image='UI/figures/xy_red.jpg')
+    imageStim6red = visual.ImageStim(main_win, size=(300, 300), pos=(600, -200), units='pix', image='UI/figures/xy_red.jpg')
+
+
+    frequencies = [25,26,27,28,29,30]           #[9,10,11,12,13,14]   #[30,31,32,33,34,35]    [25,26,27,28,29,30]
+    # 生成方波数据
+    square_wave_9 = generate_square_wave(frequencies[0], REFRESH_RATE, 5)
+    square_wave_11 = generate_square_wave(frequencies[1], REFRESH_RATE, 5)
+    square_wave_12 = generate_square_wave(frequencies[2], REFRESH_RATE, 5)
+    square_wave_13 = generate_square_wave(frequencies[3], REFRESH_RATE, 5)
+    square_wave_14 = generate_square_wave(frequencies[4], REFRESH_RATE, 5)
+    square_wave_15 = generate_square_wave(frequencies[5], REFRESH_RATE, 5)
+
+    # 创建刺激对象列表，便于管理
+    image_stims = [imageStim1, imageStim2, imageStim3, imageStim4, imageStim5, imageStim6]
+    txt_stims = [txtStim1, txtStim2, txtStim3, txtStim4, txtStim5, txtStim6]
+    square_waves = [square_wave_9, square_wave_11, square_wave_12, square_wave_13, square_wave_14, square_wave_15]
+
+    time.sleep(2)
+    # grating.color = 'black'
+    server = TCPServer()
+    server.start()
+    client = TCPClient('127.0.0.1', 8099)
+    client.connect()
+    print('Connected decoder_main')
+    # client.send_data('impedance', 1)
+    # time.sleep(20)
+    # client.send_data('impedance', 2)
+    client.send_data('targetFreqs', frequencies)  # 使用frequencies变量，确保与刺激频率一致
+    time.sleep(1)
+    # 开启全程数据保存到 EEGFiles
+    client.send_data('saveData',1)
+    # client.send_data('impedance',1)
+
+
+
+    # 实验参数
+    repeats = 3
+    seq_freq = frequencies * repeats
+    seq_freq = np.random.permutation(seq_freq).tolist()
+    num_trials = len(seq_freq)  # 总试验次数, 6*6=36
+    trial_count = 0
+    
+    # 在线解码精度计算
+    online_results = []  # 存储每个trial的解码结果
+    correct_predictions = 0  # 正确预测计数
+    
+    # 保存序列信息
+    seq_info = {
+         'total_trials': num_trials,
+         'frequencies': frequencies,
+         'sequence': seq_freq,
+         'start_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    }
+    # 保存序列信息到文件
+    import json
+    seq_file_path = f'EEGFiles/sequence_{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}.json'
+    with open(seq_file_path, 'a', encoding='utf-8') as f:
+        json.dump(seq_info, f, ensure_ascii=False, indent=2)
+
+
+#========================Trials Started======================#
+    while trial_count < num_trials:
+     # 从序列中获取当前试验的目标频率
+     target_freq = seq_freq[trial_count]
+     target_freq_index = frequencies.index(target_freq)
+     print(f'Trials {trial_count + 1}/{num_trials} - Target Frequency: {target_freq}Hz (Label: {target_freq_index + 1})')
+
+     # Stage 1: Cue Stage
+     # print('Cue Stage: The target frequency is in Red')
+     client.send_data('setLabelAndTrialInfo', {
+         'label': 0,
+         'trial_info': {
+             'trial': trial_count + 1,
+             'phase': 'cue',
+             'target_freq': target_freq
+         }
+     })
+
+     for frameN in range(int(1 * REFRESH_RATE)):  # 1秒提示
+         key_press = get_keypress()
+         if key_press in ['q']:
+             shutdown(main_win, client)
+
+         # 显示所有刺激，目标刺激为红色
+         for i, stim in enumerate(image_stims):
+             if i == target_freq_index:
+                 # 目标刺激显示红色
+                 if i == 0:
+                     imageStim1red.draw()
+                 elif i == 1:
+                     imageStim2red.draw()
+                 elif i == 2:
+                     imageStim3red.draw()
+                 elif i == 3:
+                     imageStim4red.draw()
+                 elif i == 4:
+                     imageStim5red.draw()
+                 elif i == 5:
+                     imageStim6red.draw()
+             else:
+                 # 其他刺激显示正常颜色
+                 stim.draw()
+
+         main_win.flip()
+
+     # Stage 2: Flanker Stimulus
+     # print('Flanker Stage: flank all frequencies')
+     client.send_data('predict', 1)
+     client.send_data('setLabelAndTrialInfo', {
+         'label': target_freq_index + 1,  # 设置目标频率标签   这里+1，是因为0代表不记录数据
+         'trial_info': {
+             'trial': trial_count + 1,         # trial 从0开始
+             'phase': 'stimulus',
+             'target_freq': target_freq
+         }
+     })
+     outlet.push_sample(['S  1'])
+
+     for frameN in range(6 * REFRESH_RATE):  # 6秒刺激
+         key_press = get_keypress()
+         if key_press in ['q']:
+             shutdown(main_win, client)
+
+         # 所有频率按照方波闪烁
+         if square_wave_9[frameN % len(square_wave_9)] == 1:
+             imageStim1.draw()
+         if square_wave_11[frameN % len(square_wave_11)] == 1:
+             imageStim2.draw()
+         if square_wave_12[frameN % len(square_wave_12)] == 1:
+             imageStim3.draw()
+         if square_wave_13[frameN % len(square_wave_13)] == 1:
+             imageStim4.draw()
+         if square_wave_14[frameN % len(square_wave_14)] == 1:
+             imageStim5.draw()
+         if square_wave_15[frameN % len(square_wave_15)] == 1:
+             imageStim6.draw()
+
+         main_win.flip()
+         if server.ChoosenNum != -1:
+             break
+
+     # 记录在线解码结果
+     predicted_freq_index = server.ChoosenNum  # 解码结果
+     predicted_freq = frequencies[predicted_freq_index] if predicted_freq_index != -1 else -1
+     
+     # 判断解码是否正确
+     is_correct = (predicted_freq_index == target_freq_index) if predicted_freq_index != -1 else False
+     if is_correct:
+         correct_predictions += 1
+     
+     # 记录trial结果
+     trial_result = {
+         'trial': trial_count + 1,
+         'target_freq': target_freq,
+         'target_freq_index': target_freq_index,
+         'predicted_freq': predicted_freq,
+         'predicted_freq_index': predicted_freq_index,
+         'is_correct': is_correct,
+         'status': 'Success' if predicted_freq_index != -1 else 'Failed'
+     }
+     online_results.append(trial_result)
+     
+     # 打印当前trial结果
+     status_symbol = "✓" if is_correct else "✗"
+     if predicted_freq_index == -1:
+         print(f'Trial {trial_count + 1}: 目标{target_freq}Hz -> 解码失败 - {status_symbol}')
+     else:
+         print(f'Trial {trial_count + 1}: 目标{target_freq}Hz -> 预测{predicted_freq}Hz - {status_symbol}')
+
+
+     # Stage 3: Decoding Feedback
+     outlet.push_sample(['S  2'])
+     client.send_data('setLabelAndTrialInfo', {
+         'label': 0,  # 反馈阶段标签为0
+         'trial_info': {
+             'trial': trial_count + 1,
+             'phase': 'feedback',
+             'target_freq': target_freq
+         }
+     })
+     # print('反馈阶段: 显示解码结果')
+
+     for frameN in range(1 * REFRESH_RATE):  # 1秒反馈
+         key_press = get_keypress()
+         if key_press in ['q']:
+             shutdown(main_win, client)
+
+         # 显示所有刺激但不闪烁
+         for stim in image_stims:
+             stim.draw()
+
+         # 显示解码结果
+         if server.ChoosenNum == 0:
+             txtStim1.draw()
+         elif server.ChoosenNum == 1:
+             txtStim2.draw()
+         elif server.ChoosenNum == 2:
+             txtStim3.draw()
+         elif server.ChoosenNum == 3:
+             txtStim4.draw()
+         elif server.ChoosenNum == 4:
+             txtStim5.draw()
+         elif server.ChoosenNum == 5:
+             txtStim6.draw()
+
+         main_win.flip()
+
+     server.ChoosenNum = -1
+     trial_count += 1
+
+# 计算总体在线解码精度
+total_trials = len(online_results)
+successful_trials = len([r for r in online_results if r['status'] == 'Success'])
+failed_trials = len([r for r in online_results if r['status'] == 'Failed'])
+overall_accuracy = correct_predictions / total_trials if total_trials > 0 else 0
+
+# Print Accuracy
+print(f"Total Accuracy: {overall_accuracy:.3f} ({correct_predictions}/{total_trials})")
+
+# 按频率分析准确率
+print(f"\n=== 按频率分析准确率 ===")
+freq_accuracy = {}
+for result in online_results:
+    freq = result['target_freq']
+    if freq not in freq_accuracy:
+        freq_accuracy[freq] = {'correct': 0, 'total': 0, 'failed': 0}
+    
+    freq_accuracy[freq]['total'] += 1
+    if result['status'] == 'Failed':
+        freq_accuracy[freq]['failed'] += 1
+    elif result['is_correct']:
+        freq_accuracy[freq]['correct'] += 1
+
+print(f"{'频率':<8} {'准确率':<8} {'正确/总数':<10} {'失败数':<8}")
+print("-" * 40)
+for freq in sorted(freq_accuracy.keys()):
+    stats = freq_accuracy[freq]
+    accuracy = stats['correct'] / stats['total'] if stats['total'] > 0 else 0
+    print(f"{freq}Hz{'':<4} {accuracy:.3f}{'':<4} {stats['correct']}/{stats['total']}{'':<6} {stats['failed']}")
+
+# 保存在线解码结果到文件
+online_results_file = f'EEGFiles/online_results_{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}.json'
+online_summary = {
+    'total_trials': total_trials,
+    'successful_trials': successful_trials,
+    'failed_trials': failed_trials,
+    'correct_predictions': correct_predictions,
+    'overall_accuracy': overall_accuracy,
+    # 'freq_accuracy': freq_accuracy,
+    'trial_results': online_results,
+    # 'end_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+}
+
+with open(online_results_file, 'w', encoding='utf-8') as f:
+    json.dump(online_summary, f, ensure_ascii=False, indent=2)
+
+
+client.send_data('predict',2)  # 关闭系统
+main_win.close()

verify_datamock.py

@@ -0,0 +1,304 @@
+"""
+datamock 验证脚本（模拟算法端）
+作为 ZMQ ROUTER 监听 8100 端口，等待 datamock.py 连接并验证数据流
+
+运行顺序:
+  第一步: python verify_datamock.py   (先启动，监听 8100)
+  第二步: python datamock.py          (后启动，连接 8100)
+"""
+import zmq
+import numpy as np
+import time
+import sys
+import matplotlib
+matplotlib.use('TkAgg')
+
+# 在导入 pyplot 之前确保 Tkinter 正确初始化
+try:
+    import tkinter as tk
+    root = tk.Tk()
+    root.withdraw()  # 隐藏主窗口，我们只需要它的事件循环
+except Exception as e:
+    print(f"[WARN] Tkinter 初始化警告: {e}")
+
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+# ===== 可视化参数 =====
+PLOT_WINDOW_SEC = 2.0      # 滑动窗口时长（秒）
+PLOT_CHANNELS = [0, 1, 2, 3]  # 要显示的 EEG 通道索引
+
+SERVER_ADDR = 'tcp://127.0.0.1:8100'
+FS = 250
+N_SAMPLES_PER_PKT = 5
+N_CHAN = 66
+EEG_FREQ = 10
+EEG_AMP = 100.0        # EEG 幅值 100μV（峰值）
+EEG_AMP_MEAN = EEG_AMP * 2 / np.pi  # 正弦波 |mean| ≈ 63.7μV
+EEG_AMP_TOLERANCE = 1.5             # 幅值容差倍数
+LABEL_INTERVAL = 5
+FFT_SAMPLES = 250           # 做一次 FFT 需要的采样点数（1s数据）
+EXPECTED_BYTES = N_SAMPLES_PER_PKT * N_CHAN * 4  # 1320 bytes (5*66*4)
+
+
+def validate_fft(samples):
+    """对 Ch0 数据做 FFT，返回峰值频率"""
+    freqs = np.fft.rfftfreq(FFT_SAMPLES, d=1 / FS)
+    fft_mag = np.abs(np.fft.rfft(samples))
+    peak_idx = np.argmax(fft_mag[1:]) + 1  # 跳过 DC
+    return freqs[peak_idx], fft_mag, freqs
+
+
+def main():
+    ctx = zmq.Context()
+    sock = ctx.socket(zmq.ROUTER)
+    sock.bind(SERVER_ADDR)
+    print(f"[{datetime.now().strftime('%H:%M:%S')}] ZMQ ROUTER 绑定 {SERVER_ADDR}，等待 datamock.py 连接...\n")
+
+    # ===== 初始化交互式绘图 =====
+    plt.ion()  # 开启交互模式
+    fig = plt.figure(figsize=(14, 10))
+    fig.suptitle('EEG Data Monitor (Real-time)', fontsize=14)
+
+    # 使用 GridSpec 进行布局
+    from matplotlib.gridspec import GridSpec
+    gs = GridSpec(len(PLOT_CHANNELS) + 2, 1, figure=fig, hspace=0.3)
+    axes = []
+    lines_eeg = []
+    for i, ch in enumerate(PLOT_CHANNELS):
+        ax = fig.add_subplot(gs[i])
+        axes.append(ax)
+        ax.set_ylabel(f'Ch{ch} (μV)', fontsize=8)
+        ax.grid(True, alpha=0.3)
+        ax.set_ylim(-150, 150)
+        line, = ax.plot([], [], lw=0.8)
+        lines_eeg.append(line)
+        ax.set_title(f'EEG Channel {ch}', fontsize=9)
+
+    # 标签通道子图 (Ch64 - 标签值)
+    ax_label = fig.add_subplot(gs[len(PLOT_CHANNELS)])
+    axes.append(ax_label)
+    ax_label.set_ylabel('Label Value', fontsize=8)
+    ax_label.grid(True, alpha=0.3)
+    ax_label.set_ylim(-0.5, 2.5)
+    line_label, = ax_label.plot([], [], 'ro-', lw=1.5, markersize=4)
+    line_label_data = line_label
+    ax_label.set_title('Ch64 - Label Value', fontsize=9)
+
+    # Ch65 标签序号子图
+    ax_seq = fig.add_subplot(gs[len(PLOT_CHANNELS) + 1])
+    axes.append(ax_seq)
+    ax_seq.set_ylabel('Label Seq', fontsize=8)
+    ax_seq.set_xlabel('Time (samples)', fontsize=8)
+    ax_seq.grid(True, alpha=0.3)
+    ax_seq.set_ylim(-0.5, 10)
+    line_seq, = ax_seq.plot([], [], 'gs-', lw=1.5, markersize=4)
+    line_seq_data = line_seq
+    ax_seq.set_title('Ch65 - Label Sequence', fontsize=9)
+
+    plt.tight_layout()
+
+    # ===== 状态 =====
+    global_idx = 0          # 全局采样点索引
+    label_events = []       # 捕获的标签事件
+    start_time = None
+    fft_done = False
+    fft_buffer = []         # 暂存前 250 点做 FFT
+    ch64_zero_ok = True     # 验证 Ch64 非标签采样点均为 0
+    ch65_zero_ok = True     # 验证 Ch65 非标签采样点均为 0
+    label_pos_ok_all = True # 验证标签均在包内索引 4
+
+    # ===== 数据缓冲区 =====
+    max_samples = int(FS * PLOT_WINDOW_SEC)
+    eeg_buffer = {ch: np.zeros(max_samples) for ch in PLOT_CHANNELS}
+    label_buffer = np.zeros(max_samples)
+    seq_buffer = np.zeros(max_samples)
+    time_axis = np.arange(max_samples)
+
+    # ZMQ 收发统计
+    recv_count = 0
+
+    try:
+        # 首次 pause 用于显示窗口
+        plt.pause(0.5)
+        print(f"[INFO] 交互窗口已显示，如未看到请检查任务栏")
+
+        while True:
+            # ROUTER recv: prepended 一个 identity 帧
+            # datamock 发送 3帧 [b'datamock', b'', data_bytes]
+            # ROUTER 接收后变成 4帧 [router_identity, b'datamock', b'', data_bytes]
+            frames = sock.recv_multipart()
+            recv_count += 1
+            now = time.time()
+            if start_time is None:
+                start_time = now
+
+            # 帧格式: [router_identity, b'datamock', b'', data_bytes]
+            router_id = frames[0]   # ROUTER 添加的身份帧
+            identity = frames[1]     # 发送端的 identity
+            _empty = frames[2]      # 空帧
+            raw_data = frames[3]     # 实际数据字节
+
+            # 数据长度校验
+            if len(raw_data) != EXPECTED_BYTES:
+                print(f"[ERROR] 数据长度错误: 期望{EXPECTED_BYTES}字节, 实际{len(raw_data)}字节")
+                continue
+
+            # 解析为 [5, 66] float32 数组
+            packet = np.frombuffer(raw_data, dtype=np.float32).reshape(N_SAMPLES_PER_PKT, N_CHAN)
+
+            elapsed = now - start_time
+
+            # ===== 验证 1: 数据形状 =====
+            if recv_count == 1:
+                shape_ok = packet.shape == (N_SAMPLES_PER_PKT, N_CHAN)
+                print(f"[{'✓' if shape_ok else '✗'}] 数据形状: {packet.shape}  "
+                      f"(期望 [{N_SAMPLES_PER_PKT}, {N_CHAN}])")
+                if not shape_ok:
+                    print(f"    ✗ 形状不匹配，退出")
+                    break
+
+            # ===== 验证 2: EEG 幅值（首包） =====
+            if recv_count == 1:
+                eeg = packet[:, :64]
+                amp_mean = np.mean(np.abs(eeg))
+                amp_ok = amp_mean <= EEG_AMP_MEAN * EEG_AMP_TOLERANCE
+                print(f"[{'✓' if amp_ok else '✗'}] EEG 幅值: 均值={amp_mean:.2f}μV  "
+                      f"(期望 ~{EEG_AMP_MEAN:.2f}μV，峰值 ~{EEG_AMP:.2f}μV)")
+                if not amp_ok:
+                    print(f"    ✗ 幅值超出容差范围")
+
+            # ===== 验证 3: EEG 频率（首秒数据收集满后做 FFT） =====
+            fft_buffer.append(packet[:, 0].copy())  # 收集 Ch0
+
+            if not fft_done and len(fft_buffer) * N_SAMPLES_PER_PKT >= FFT_SAMPLES:
+                # 凑够 250 点，做 FFT
+                all_ch0 = np.concatenate(fft_buffer)[:FFT_SAMPLES]
+                peak_freq, fft_mag, freqs = validate_fft(all_ch0)
+                freq_ok = abs(peak_freq - EEG_FREQ) < 1.0
+
+                print(f"[{'✓' if freq_ok else '✗'}] EEG 频率: 峰值={peak_freq:.1f}Hz  "
+                      f"(期望 ~{EEG_FREQ}Hz)")
+                print(f"    FFT 幅度谱前 5 峰值:")
+                top5 = np.argsort(fft_mag[1:])[-5:][::-1] + 1
+                for rank, idx in enumerate(top5):
+                    print(f"      {rank+1}. {freqs[idx]:.1f}Hz  幅度={fft_mag[idx]:.1f}")
+                print()
+                fft_done = True
+
+            # ===== 验证 4: 标签通道（Ch64/Ch65） =====
+            ch64 = packet[:, 64]
+            ch65 = packet[:, 65]
+            ch64_nonzero = np.where(ch64 != 0)[0]
+            ch65_nonzero = np.where(ch65 != 0)[0]
+
+            # 检查非标签采样点是否全为 0
+            ch64_zeros = np.all(ch64[:4] == 0)
+            ch65_zeros = np.all(ch65[:4] == 0)
+            ch64_zero_ok = ch64_zero_ok and ch64_zeros
+            ch65_zero_ok = ch65_zero_ok and ch65_zeros
+
+            if len(ch64_nonzero) > 0:
+                pos_in_pkt = int(ch64_nonzero[0])
+                label_val = int(ch64[pos_in_pkt])
+                label_seq = int(ch65[pos_in_pkt])
+
+                pos_ok = (len(ch64_nonzero) == 1 and pos_in_pkt == 4)
+                label_pos_ok_all = label_pos_ok_all and pos_ok
+
+                elapsed_since_start = now - start_time
+                print(f"[✓] 标签触发 @ {elapsed_since_start:.1f}s  "
+                      f"(global_idx={global_idx}  包{recv_count})")
+                print(f"    Ch64 标签值: {label_val}  Ch65 序号: {label_seq}")
+                print(f"    包内位置: 采样点 {pos_in_pkt}/4  "
+                      f"({'✓' if pos_ok else '✗ 期望 4'})  "
+                      f"其余采样点 Ch64=0: {'✓' if ch64_zeros else '✗'}  "
+                      f"Ch65=0: {'✓' if ch65_zeros else '✗'}")
+                print()
+
+                label_events.append({
+                    'time': elapsed_since_start,
+                    'label': label_val,
+                    'seq': label_seq
+                })
+
+            global_idx += N_SAMPLES_PER_PKT
+
+            # ===== 更新绘图缓冲区 =====
+            for ch_idx, ch in enumerate(PLOT_CHANNELS):
+                eeg_buffer[ch] = np.roll(eeg_buffer[ch], -N_SAMPLES_PER_PKT)
+                eeg_buffer[ch][-N_SAMPLES_PER_PKT:] = packet[:, ch]
+
+            label_buffer = np.roll(label_buffer, -N_SAMPLES_PER_PKT)
+            label_buffer[-N_SAMPLES_PER_PKT:] = packet[:, 64]
+
+            seq_buffer = np.roll(seq_buffer, -N_SAMPLES_PER_PKT)
+            seq_buffer[-N_SAMPLES_PER_PKT:] = packet[:, 65]
+
+            # ===== 实时更新绘图 =====
+            for i, ch in enumerate(PLOT_CHANNELS):
+                lines_eeg[i].set_data(time_axis, eeg_buffer[ch])  # 数据已是 μV 单位
+            line_label_data.set_data(time_axis, label_buffer)
+            line_seq_data.set_data(time_axis, seq_buffer)
+
+            # 设置 x 轴范围
+            for ax in axes:
+                ax.set_xlim(0, max_samples)
+
+            # 刷新图形（交互模式）
+            fig.canvas.draw_idle()
+            plt.pause(0.001)
+
+    except KeyboardInterrupt:
+        print("\n" + "=" * 55)
+        print("  验证结果汇总")
+        print("=" * 55)
+        print(f"  运行时长: {time.time() - start_time:.1f}s")
+        print(f"  收到包数: {recv_count}")
+        print(f"  FFT 验证: {'✓ 已完成' if fft_done else '✗ 未完成（时长不足1s）'}")
+        print(f"  非标签采样点 Ch64=0: {'✓' if ch64_zero_ok else '✗'}")
+        print(f"  非标签采样点 Ch65=0: {'✓' if ch65_zero_ok else '✗'}")
+        print(f"  标签均在包内位置4: {'✓' if label_pos_ok_all else '✗'}")
+
+        if label_events:
+            print(f"\n  共捕获 {len(label_events)} 次标签事件:")
+            for i, ev in enumerate(label_events):
+                print(f"    {i+1}. t={ev['time']:.1f}s  label={ev['label']}  序号={ev['seq']}")
+
+            # 标签间隔
+            print(f"\n  标签间隔验证 (期望 ~{LABEL_INTERVAL}s):")
+            for i in range(1, len(label_events)):
+                dt = label_events[i]['time'] - label_events[i-1]['time']
+                ok = abs(dt - LABEL_INTERVAL) < 0.1
+                print(f"    {i}->{i+1}: {dt:.2f}s  {'✓' if ok else '✗'}")
+
+            # 标签交替
+            labels = [e['label'] for e in label_events]
+            alt_ok = all(labels[i] != labels[i+1] for i in range(len(labels) - 1))
+            print(f"\n  标签交替: {labels}  {'✓ 交替正确' if alt_ok else '✗ 交替错误'}")
+
+            # 序号
+            label1_seqs = [e['seq'] for e in label_events if e['label'] == 1]
+            label2_seqs = [e['seq'] for e in label_events if e['label'] == 2]
+            s1_ok = label1_seqs == list(range(1, len(label1_seqs) + 1))
+            s2_ok = label2_seqs == list(range(1, len(label2_seqs) + 1))
+            print(f"  label=1 序号: {label1_seqs}  {'✓' if s1_ok else '✗'}")
+            print(f"  label=2 序号: {label2_seqs}  {'✓' if s2_ok else '✗'}")
+        else:
+            print(f"\n  未捕获标签事件（运行时长不足 {LABEL_INTERVAL}s）")
+
+        print("=" * 55)
+
+    finally:
+        sock.close()
+        ctx.term()
+        plt.ioff()
+        plt.close('all')
+        try:
+            root.destroy()
+        except:
+            pass
+
+
+if __name__ == '__main__':
+    main()