910 lines
32 KiB
Python
910 lines
32 KiB
Python
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# -*- coding: utf-8 -*-
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from __future__ import annotations
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"""
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run_metrics_and_figs.py
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1) 自动读取 mat_dir 中排序后的第一个 .mat
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2) 调用模型预测（HC/MDD）并写 ResultData.txt
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3) 同时保存图片：EEG.png / psd.png / average_topomap.png / topomaps.png
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"""
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import matplotlib
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matplotlib.use('Agg')
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import numpy as np
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import os
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import shutil
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import scipy.io
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import scipy.signal as signal
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import matplotlib.pyplot as plt
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import mne
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from mne.preprocessing import ICA
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# ==========================
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# Config
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# ==========================
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PREPROCESS_BANDPASS = (0.8, 30.0)
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PREPROCESS_NOTCH = [50, 100]
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PREPROCESS_ICA_N = 0.99
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PREPROCESS_ICA_SEED = 97
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PREPROCESS_APPLY_AVG_REF = True
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PREPROCESS_BAD_PTP_UV = 350.0 # 坏段阈值 (μV)
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DEFAULT_FS = 250.0
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EEG_PLOT_SECONDS = 10
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PSD_FMIN, PSD_FMAX = 0.8, 45.0
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EPS = 1e-12
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FIXED_EEG_IDXS = [23, 47, 39, 6, 2, 21, 35, 57] # 0-based index, 按重要性排序
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FIXED_EEG_LABELS = ["C5", "O1", "TP7", "FPZ", "PO6", "P4", "AF7", "AF3"]
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BANDS_METRICS = {
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"Delta": (1.0, 4.0),
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"Theta": (4.0, 8.0),
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"Alpha": (8.0, 13.0),
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"Beta": (13.0, 30.0),
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}
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TOTAL_POWER_BAND = (1.0, 50.0)
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BANDS_TOPOMAP = {
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"delta": (0.8, 3.9),
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"theta": (4.0, 7.9),
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"alpha": (8.0, 12.9),
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"beta": (13.0, 30.0),
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"broad": (0.8, 30.0),
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}
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# ==========================
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# 预处理逻辑
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# ==========================
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def annotate_bad_segments(raw, peak_to_peak_uv=250.0):
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"""
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简单坏段检测：按固定窗口计算峰峰值，超过阈值标为 bad。
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"""
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peak_to_peak_v = peak_to_peak_uv * 1e-6
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win = int(raw.info["sfreq"] * 1.0)
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step = int(raw.info["sfreq"] * 0.5)
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data = raw.get_data()
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n_times = data.shape[1]
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onsets = []
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durations = []
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descriptions = []
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for start in range(0, n_times - win, step):
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seg = data[:, start:start + win]
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ptp = np.ptp(seg, axis=1)
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if np.any(ptp > peak_to_peak_v):
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onsets.append(start / raw.info["sfreq"])
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durations.append(win / raw.info["sfreq"])
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descriptions.append("BAD_PTP")
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if len(onsets) > 0:
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ann = mne.Annotations(onset=onsets, duration=durations, description=descriptions)
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raw.set_annotations(ann)
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print(f"[INFO] Annotated bad segments: {len(onsets)} windows")
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else:
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print("[INFO] No bad segments detected by PTP rule")
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def run_preprocess_on_raw(raw: mne.io.RawArray) -> mne.io.RawArray:
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"""
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核心预处理：滤波 + 平均参考 + 坏段标注 + ICA
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"""
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# 1) 滤波
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raw.filter(PREPROCESS_BANDPASS[0], PREPROCESS_BANDPASS[1], fir_design="firwin", verbose=False)
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raw.notch_filter(PREPROCESS_NOTCH, fir_design="firwin", verbose=False)
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# 2) 平均参考
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if PREPROCESS_APPLY_AVG_REF:
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raw.set_eeg_reference("average", verbose=False)
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# 3) 坏段标注
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annotate_bad_segments(raw, peak_to_peak_uv=PREPROCESS_BAD_PTP_UV)
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# 4) ICA
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ica = ICA(
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n_components=PREPROCESS_ICA_N,
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random_state=PREPROCESS_ICA_SEED,
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max_iter=800,
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method="fastica"
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)
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ica.fit(raw, reject_by_annotation=True, verbose=False)
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try:
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eog_inds, _ = ica.find_bads_eog(raw, verbose=False)
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if eog_inds:
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ica.exclude.extend(eog_inds)
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print(f"[INFO] ICA exclude EOG comps: {eog_inds}")
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except Exception as e:
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print(f"[WARN] ICA find_bads_eog skipped: {e}")
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raw_clean = ica.apply(raw.copy(), verbose=False)
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return raw_clean
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def preprocess_mat_file(src_mat_path: str, temp_out_dir: str) -> str:
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"""
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读取原始mat -> 预处理 -> 保存到 temp_out_dir -> 返回新路径
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"""
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os.makedirs(temp_out_dir, exist_ok=True)
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# 1. 读原始 mat
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# 注意：这里我们只要数据部分转成 MNE Raw，然后处理，再存回
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# 复用现有的 load_eeg_from_mat 拿到 ndarray
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eeg_uV, fs, ch_names, xyz = load_eeg_from_mat(src_mat_path)
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# 转 MNE (注意单位：uV -> V)
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if not ch_names:
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ch_names = [f"CH{i+1}" for i in range(eeg_uV.shape[1])]
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info = mne.create_info(ch_names=ch_names, sfreq=fs, ch_types=["eeg"] * len(ch_names))
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raw = mne.io.RawArray(eeg_uV.T * 1e-6, info, verbose=False)
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if xyz is not None and isinstance(xyz, np.ndarray):
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# 尝试设 montage（虽然对滤波不关键，但尽量保留信息）
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try:
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ch_pos = {ch_names[i]: xyz[i, :] for i in range(len(ch_names))}
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montage = mne.channels.make_dig_montage(ch_pos=ch_pos, coord_frame="head")
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raw.set_montage(montage, on_missing="ignore")
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except Exception:
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pass
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# 2. 执行预处理
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print(f"[INFO] Start preprocessing: {src_mat_path}")
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raw_clean = run_preprocess_on_raw(raw)
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# 3. 存回 .mat (保持结构兼容，以便后续 run_all 读取)
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# 这里我们需要读取原始 mat 的结构体，把 data 替换掉
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try:
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mat_struct = scipy.io.loadmat(src_mat_path, struct_as_record=False, squeeze_me=True)
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if "eeg" in mat_struct:
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eeg_obj = mat_struct["eeg"]
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# 替换数据：MNE (V) -> uV -> (T, C)
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clean_data_uV = (raw_clean.get_data() * 1e6).T
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eeg_obj.data = clean_data_uV
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base_name = os.path.basename(src_mat_path)
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new_path = os.path.join(temp_out_dir, base_name)
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scipy.io.savemat(new_path, {"eeg": eeg_obj}, do_compression=True)
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print(f"[INFO] Preprocessed file saved to: {new_path}")
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return new_path
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except Exception as e:
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print(f"[WARN] Failed to preserve original struct structure: {e}")
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# Fallback: 如果读原始结构失败，就存一个简单的 mat
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clean_data_uV = (raw_clean.get_data() * 1e6).T
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out_dict = {
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"eeg": {
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"data": clean_data_uV,
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"sample_rate": fs,
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"electrode_name": ch_names,
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"electrode_xyz": xyz if xyz is not None else []
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}
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}
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base_name = os.path.basename(src_mat_path)
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new_path = os.path.join(temp_out_dir, base_name)
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scipy.io.savemat(new_path, out_dict, do_compression=True)
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print(f"[INFO] Preprocessed file saved (fallback mode) to: {new_path}")
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return new_path
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# ==========================
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# 输出目录
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# ==========================
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def ensure_outdir(out_root: str) -> str:
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"""
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确保输出目录存在，并清空除 ResultData.txt 之外的旧文件。
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不再创建 timestamp 子文件夹，直接输出到 out_root。
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"""
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if os.path.exists(out_root):
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# 清空目录，但保留 ResultData.txt
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for filename in os.listdir(out_root):
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if filename == "ResultData.txt":
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continue
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file_path = os.path.join(out_root, filename)
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try:
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if os.path.isfile(file_path) or os.path.islink(file_path):
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os.unlink(file_path)
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elif os.path.isdir(file_path):
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shutil.rmtree(file_path)
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except Exception as e:
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print(f"[WARN] Failed to delete {file_path}. Reason: {e}")
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else:
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os.makedirs(out_root, exist_ok=True)
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return out_root
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# ==========================
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# 单位自动识别：统一到 μV
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# ==========================
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def _auto_scale_to_uV(data_nt_nc: np.ndarray):
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data = np.asarray(data_nt_nc)
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p95 = float(np.percentile(np.abs(data), 95))
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if p95 <= 0.5:
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data_uV = data * 1e6
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msg = f"[UNIT] p95={p95:.3g} -> assume V, convert to μV by *1e6"
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elif p95 > 5000:
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data_uV = data * 1e-3
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msg = f"[UNIT] p95={p95:.3g} -> assume nV, convert to μV by /1000"
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else:
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data_uV = data
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msg = f"[UNIT] p95={p95:.3g} -> assume μV, no scaling"
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p95_uV = float(np.percentile(np.abs(data_uV), 95))
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warn = None
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if p95_uV > 5000:
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warn = f"[WARN] After scaling, p95 still large: {p95_uV:.3g} μV"
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elif p95_uV < 0.1:
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warn = f"[WARN] After scaling, p95 still small: {p95_uV:.3g} μV"
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return data_uV, msg, warn
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# ==========================
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# mat 读取（支持 struct.data / electrode_name / electrode_xyz / sample_rate）
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# ==========================
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def _unwrap_singleton(x):
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while True:
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if isinstance(x, np.ndarray):
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if x.dtype == object and x.size == 1:
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x = x.item()
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continue
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if x.size == 1 and x.ndim >= 1:
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try:
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x = x.reshape(-1)[0]
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continue
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except Exception:
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pass
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break
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return x
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def _try_get_struct_field(v, field_name="data"):
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if hasattr(v, "_fieldnames") and field_name in getattr(v, "_fieldnames", []):
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return getattr(v, field_name)
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if isinstance(v, np.ndarray) and v.dtype.names and field_name in v.dtype.names:
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try:
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return v[field_name]
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except Exception:
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return None
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return None
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def _extract_electrode_names(st):
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nf = _try_get_struct_field(st, "electrode_name")
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if nf is None:
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return None
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nf = _unwrap_singleton(nf)
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if isinstance(nf, (list, tuple)):
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names = [str(x).strip() for x in nf]
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return names if names else None
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if isinstance(nf, np.ndarray):
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flat = nf.reshape(-1)
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names = [str(_unwrap_singleton(x)).strip() for x in flat]
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return names if names else None
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s = str(nf).strip()
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return [s] if s else None
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def _extract_sample_rate(st):
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sr = _try_get_struct_field(st, "sample_rate")
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if sr is None:
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return None
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sr = _unwrap_singleton(sr)
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try:
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return float(sr)
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except Exception:
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return None
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def _extract_xyz(st):
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xyz = _try_get_struct_field(st, "electrode_xyz")
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if xyz is None:
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return None
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xyz = _unwrap_singleton(xyz)
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try:
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xyz = np.asarray(xyz, dtype=float)
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if xyz.ndim == 2 and xyz.shape[1] == 3:
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return xyz
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if xyz.ndim == 2 and xyz.shape[0] == 3:
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return xyz.T
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return None
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except Exception:
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return None
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def load_eeg_from_mat(mat_path: str):
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mat = scipy.io.loadmat(mat_path, struct_as_record=False, squeeze_me=True)
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candidates = []
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st_for_meta = None
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for k, v in mat.items():
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if k.startswith("__"):
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continue
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|
|
if isinstance(v, np.ndarray) and v.ndim == 2 and np.issubdtype(v.dtype, np.number):
|
|||
|
|
candidates.append((k, v, None))
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
data_field = _try_get_struct_field(v, "data")
|
|||
|
|
if data_field is not None:
|
|||
|
|
data_field = _unwrap_singleton(data_field)
|
|||
|
|
if isinstance(data_field, np.ndarray) and data_field.ndim == 2:
|
|||
|
|
candidates.append((f"{k}.data", data_field, v))
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
if isinstance(v, np.ndarray) and v.dtype == object:
|
|||
|
|
vv = _unwrap_singleton(v)
|
|||
|
|
if isinstance(vv, np.ndarray) and vv.ndim == 2 and np.issubdtype(vv.dtype, np.number):
|
|||
|
|
candidates.append((k, vv, None))
|
|||
|
|
continue
|
|||
|
|
data2 = _try_get_struct_field(vv, "data")
|
|||
|
|
if data2 is not None:
|
|||
|
|
data2 = _unwrap_singleton(data2)
|
|||
|
|
if isinstance(data2, np.ndarray) and data2.ndim == 2:
|
|||
|
|
candidates.append((f"{k}.data", data2, vv))
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
if not candidates:
|
|||
|
|
raise RuntimeError(f"mat 里没找到可用 EEG 二维矩阵或 struct.data：{mat_path}")
|
|||
|
|
|
|||
|
|
def score(arr: np.ndarray) -> int:
|
|||
|
|
s = 0
|
|||
|
|
if 64 in arr.shape: s += 10
|
|||
|
|
if 32 in arr.shape: s += 9
|
|||
|
|
if 128 in arr.shape: s += 8
|
|||
|
|
if 129 in arr.shape: s += 7
|
|||
|
|
s += int(np.prod(arr.shape) // 100000)
|
|||
|
|
return s
|
|||
|
|
|
|||
|
|
candidates.sort(key=lambda x: score(x[1]), reverse=True)
|
|||
|
|
key, eeg, st = candidates[0]
|
|||
|
|
st_for_meta = st
|
|||
|
|
|
|||
|
|
eeg = np.asarray(_unwrap_singleton(eeg), dtype=np.float32)
|
|||
|
|
if eeg.ndim != 2:
|
|||
|
|
raise RuntimeError(f"解析结果不是二维: key={key}, shape={eeg.shape}, file={mat_path}")
|
|||
|
|
|
|||
|
|
# 统一成 (T, C)
|
|||
|
|
if eeg.shape[0] in (32, 64, 128, 129) and eeg.shape[1] not in (32, 64, 128, 129):
|
|||
|
|
eeg = eeg.T
|
|||
|
|
elif eeg.shape[1] in (32, 64, 128, 129):
|
|||
|
|
if eeg.shape[0] in (32, 64, 128, 129) and eeg.shape[0] < eeg.shape[1]:
|
|||
|
|
eeg = eeg.T
|
|||
|
|
|
|||
|
|
fs = DEFAULT_FS
|
|||
|
|
ch_names = None
|
|||
|
|
xyz = None
|
|||
|
|
if st_for_meta is not None:
|
|||
|
|
fs2 = _extract_sample_rate(st_for_meta)
|
|||
|
|
if fs2 is not None and fs2 > 1:
|
|||
|
|
fs = float(fs2)
|
|||
|
|
ch_names = _extract_electrode_names(st_for_meta)
|
|||
|
|
xyz = _extract_xyz(st_for_meta)
|
|||
|
|
|
|||
|
|
eeg_uV, msg, warn = _auto_scale_to_uV(eeg)
|
|||
|
|
print(msg)
|
|||
|
|
if warn:
|
|||
|
|
print(warn)
|
|||
|
|
|
|||
|
|
return eeg_uV.astype(np.float32), float(fs), ch_names, xyz
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ==========================
|
|||
|
|
# 预测接口：导入 predict_hc_mdd
|
|||
|
|
# ==========================
|
|||
|
|
def _predict_label_by_model(model_path: str, mat_dir: str) -> str:
|
|||
|
|
try:
|
|||
|
|
from infer_pth import predict_hc_mdd
|
|||
|
|
except Exception as e:
|
|||
|
|
raise RuntimeError(
|
|||
|
|
"无法导入 predict_hc_mdd（请确保 pre.py 或 infer_pth.py 与本文件同目录）。\n"
|
|||
|
|
f"原始错误: {e}"
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
out = predict_hc_mdd(mat_dir, model_path)
|
|||
|
|
except TypeError:
|
|||
|
|
out = predict_hc_mdd(model_path, mat_dir)
|
|||
|
|
|
|||
|
|
label = str(out.get("pred_label", "")).strip().upper()
|
|||
|
|
if label not in ("HC", "MDD"):
|
|||
|
|
raise RuntimeError(f"predict_hc_mdd 返回 pred_label 非法: {label}，原始返回: {out}")
|
|||
|
|
return label
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ==========================
|
|||
|
|
# 通道分区
|
|||
|
|
# ==========================
|
|||
|
|
def _norm_name(s: str) -> str:
|
|||
|
|
return str(s).strip().upper().replace(" ", "")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def build_channel_index_map(ch_names, n_channels: int):
|
|||
|
|
if not ch_names or len(ch_names) != n_channels:
|
|||
|
|
return {}
|
|||
|
|
return {_norm_name(nm): i for i, nm in enumerate(ch_names)}
|
|||
|
|
|
|||
|
|
|
|||
|
|
def pick_indices_by_names(name_to_idx, names):
|
|||
|
|
idx = []
|
|||
|
|
for n in names:
|
|||
|
|
nn = _norm_name(n)
|
|||
|
|
if nn in name_to_idx:
|
|||
|
|
idx.append(name_to_idx[nn])
|
|||
|
|
return sorted(list(set(idx)))
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _fallback_region_indices(n_channels: int):
|
|||
|
|
a = int(n_channels * 0.33)
|
|||
|
|
b = int(n_channels * 0.66)
|
|||
|
|
frontal = list(range(0, a))
|
|||
|
|
central = list(range(a, b))
|
|||
|
|
parietal = list(range(b, n_channels))
|
|||
|
|
prefrontal = list(range(0, max(2, a // 2)))
|
|||
|
|
posterior = list(range(b, n_channels))
|
|||
|
|
left = [i for i in range(n_channels) if i % 2 == 0]
|
|||
|
|
right = [i for i in range(n_channels) if i % 2 == 1]
|
|||
|
|
return frontal, central, parietal, prefrontal, posterior, left, right
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_region_indices(name_to_idx, n_channels: int):
|
|||
|
|
if not name_to_idx:
|
|||
|
|
return _fallback_region_indices(n_channels)
|
|||
|
|
|
|||
|
|
central_names = ["CZ","C1","C2","C3","C4","C5","C6","CP1","CP2","CP3","CP4","CP5","CP6","FC1","FC2","FC3","FC4","FC5","FC6"]
|
|||
|
|
frontal_names = ["FZ","F1","F2","F3","F4","F5","F6","F7","F8","AF3","AF4","AF7","AF8","FPZ","FP1","FP2","FCZ"]
|
|||
|
|
parietal_names = ["PZ","P1","P2","P3","P4","P5","P6","POZ","PO3","PO4","PO5","PO6","PO7","PO8","CPZ"]
|
|||
|
|
prefrontal_names = ["FP1","FP2","FPZ","AF3","AF4","AF7","AF8"]
|
|||
|
|
posterior_names = ["O1","O2","OZ","PO7","PO8","PO3","PO4","PZ","P3","P4","P1","P2"]
|
|||
|
|
|
|||
|
|
central = pick_indices_by_names(name_to_idx, central_names)
|
|||
|
|
frontal = pick_indices_by_names(name_to_idx, frontal_names)
|
|||
|
|
parietal = pick_indices_by_names(name_to_idx, parietal_names)
|
|||
|
|
prefrontal = pick_indices_by_names(name_to_idx, prefrontal_names)
|
|||
|
|
posterior = pick_indices_by_names(name_to_idx, posterior_names)
|
|||
|
|
|
|||
|
|
left_names = ["FP1","AF3","AF7","F3","F5","F7"]
|
|||
|
|
right_names = ["FP2","AF4","AF8","F4","F6","F8"]
|
|||
|
|
left = pick_indices_by_names(name_to_idx, left_names)
|
|||
|
|
right = pick_indices_by_names(name_to_idx, right_names)
|
|||
|
|
|
|||
|
|
if not (central and frontal and parietal and prefrontal and posterior):
|
|||
|
|
fb = _fallback_region_indices(n_channels)
|
|||
|
|
frontal2, central2, parietal2, prefrontal2, posterior2, left2, right2 = fb
|
|||
|
|
frontal = frontal if frontal else frontal2
|
|||
|
|
central = central if central else central2
|
|||
|
|
parietal = parietal if parietal else parietal2
|
|||
|
|
prefrontal = prefrontal if prefrontal else prefrontal2
|
|||
|
|
posterior = posterior if posterior else posterior2
|
|||
|
|
left = left if left else left2
|
|||
|
|
right = right if right else right2
|
|||
|
|
|
|||
|
|
return frontal, central, parietal, prefrontal, posterior, left, right
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ==========================
|
|||
|
|
# Welch PSD + band power
|
|||
|
|
# ==========================
|
|||
|
|
def welch_psd(eeg_tc: np.ndarray, fs: float):
|
|||
|
|
nperseg = min(1024, eeg_tc.shape[0])
|
|||
|
|
if nperseg < 128:
|
|||
|
|
nperseg = min(256, eeg_tc.shape[0])
|
|||
|
|
freqs, pxx = signal.welch(
|
|||
|
|
eeg_tc, fs=fs, nperseg=nperseg, noverlap=nperseg // 2,
|
|||
|
|
axis=0, scaling="density",
|
|||
|
|
)
|
|||
|
|
return freqs, pxx
|
|||
|
|
|
|||
|
|
|
|||
|
|
def band_power_from_psd(freqs, pxx_fc, band):
|
|||
|
|
lo, hi = band
|
|||
|
|
m = (freqs >= lo) & (freqs < hi)
|
|||
|
|
if not np.any(m):
|
|||
|
|
return np.zeros((pxx_fc.shape[1],), dtype=np.float32)
|
|||
|
|
|
|||
|
|
# 兼容处理：numpy 2.0+ 推荐使用 trapezoid，旧版本用 trapz
|
|||
|
|
if hasattr(np, "trapezoid"):
|
|||
|
|
return np.trapezoid(pxx_fc[m, :], freqs[m], axis=0).astype(np.float32)
|
|||
|
|
else:
|
|||
|
|
return np.trapz(pxx_fc[m, :], freqs[m], axis=0).astype(np.float32)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def region_mean_power(freqs, pxx_fc, idx, band) -> float:
|
|||
|
|
if not idx:
|
|||
|
|
return 0.0
|
|||
|
|
pw = band_power_from_psd(freqs, pxx_fc, band)
|
|||
|
|
return float(np.mean(pw[idx]))
|
|||
|
|
|
|||
|
|
|
|||
|
|
def compute_iaf(freqs, pxx_fc, posterior_idx):
|
|||
|
|
lo, hi = BANDS_METRICS["Alpha"]
|
|||
|
|
m = (freqs >= lo) & (freqs <= hi)
|
|||
|
|
if not np.any(m) or not posterior_idx:
|
|||
|
|
return 0.0
|
|||
|
|
spec = np.mean(pxx_fc[:, posterior_idx], axis=1)
|
|||
|
|
sub = spec[m]
|
|||
|
|
fsub = freqs[m]
|
|||
|
|
return float(fsub[int(np.argmax(sub))])
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ==========================
|
|||
|
|
# 图：EEG波形、PSD
|
|||
|
|
# ==========================
|
|||
|
|
def plot_eeg_waveforms(data_uv_tc: np.ndarray, fs: float, ch_names, out_dir: str, seconds: int = 10):
|
|||
|
|
"""
|
|||
|
|
固定用 FIXED_EEG_IDXS 画 EEG.png（按重要性排序）
|
|||
|
|
data_uv_tc: (T, C) μV
|
|||
|
|
"""
|
|||
|
|
T, C = data_uv_tc.shape
|
|||
|
|
|
|||
|
|
# 1) 过滤越界索引（避免你的数据通道数不足时报错）
|
|||
|
|
idxs = [i for i in FIXED_EEG_IDXS if 0 <= i < C]
|
|||
|
|
if len(idxs) < len(FIXED_EEG_IDXS):
|
|||
|
|
missing = [i for i in FIXED_EEG_IDXS if not (0 <= i < C)]
|
|||
|
|
print(f"[WARN] Some fixed EEG indices out of range (C={C}): {missing}")
|
|||
|
|
|
|||
|
|
if len(idxs) == 0:
|
|||
|
|
raise RuntimeError(f"No valid indices in FIXED_EEG_IDXS for current data (C={C}).")
|
|||
|
|
|
|||
|
|
# 2) 通道显示
|
|||
|
|
picked_names = []
|
|||
|
|
for idx in idxs:
|
|||
|
|
# 找 idx 在 FIXED_EEG_IDXS 的位置，用对应标签
|
|||
|
|
pos = FIXED_EEG_IDXS.index(idx)
|
|||
|
|
std_label = FIXED_EEG_LABELS[pos] if pos < len(FIXED_EEG_LABELS) else f"CH{idx}"
|
|||
|
|
if ch_names and idx < len(ch_names):
|
|||
|
|
picked_names.append(f"{std_label}")
|
|||
|
|
else:
|
|||
|
|
picked_names.append(std_label)
|
|||
|
|
|
|||
|
|
# 3) 截取前 seconds 秒
|
|||
|
|
max_samples = int(min(T, seconds * fs))
|
|||
|
|
x = np.arange(max_samples) / fs
|
|||
|
|
|
|||
|
|
fig_h = 1.4 * len(idxs) + 1
|
|||
|
|
fig, axes = plt.subplots(len(idxs), 1, figsize=(10, fig_h), sharex=True)
|
|||
|
|
if len(idxs) == 1:
|
|||
|
|
axes = [axes]
|
|||
|
|
|
|||
|
|
# 4) 分位数定范围，避免尖峰撑爆
|
|||
|
|
seg = data_uv_tc[:max_samples, idxs].T # (n_ch, samples)
|
|||
|
|
lo = float(np.percentile(seg, 1))
|
|||
|
|
hi = float(np.percentile(seg, 99))
|
|||
|
|
m = max(abs(lo), abs(hi))
|
|||
|
|
m = max(m, 50.0)
|
|||
|
|
|
|||
|
|
for ax, ch_idx, nm in zip(axes, idxs, picked_names):
|
|||
|
|
y = data_uv_tc[:max_samples, ch_idx]
|
|||
|
|
ax.plot(x, y, linewidth=1.2)
|
|||
|
|
ax.set_ylabel("μV")
|
|||
|
|
ax.set_title(nm, loc="left", fontsize=10)
|
|||
|
|
ax.grid(True, alpha=0.3)
|
|||
|
|
ax.set_ylim(-m, m)
|
|||
|
|
|
|||
|
|
axes[-1].set_xlabel("Time (s)")
|
|||
|
|
plt.tight_layout()
|
|||
|
|
|
|||
|
|
out_path = os.path.join(out_dir, "EEG.png")
|
|||
|
|
plt.savefig(out_path, dpi=200)
|
|||
|
|
plt.close(fig)
|
|||
|
|
print(f"[OK] EEG waveform saved: {out_path}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def plot_psd(eeg_uV_tc, fs, ch_names, out_dir):
|
|||
|
|
C = eeg_uV_tc.shape[1]
|
|||
|
|
chosen_idx = []
|
|||
|
|
|
|||
|
|
if ch_names:
|
|||
|
|
mp = {n.upper(): i for i, n in enumerate(ch_names)}
|
|||
|
|
for p in ["C3","C4","CZ"]:
|
|||
|
|
if p in mp:
|
|||
|
|
chosen_idx.append(mp[p])
|
|||
|
|
if len(chosen_idx) < 3:
|
|||
|
|
stds = [(i, float(np.std(eeg_uV_tc[:, i]))) for i in range(C)]
|
|||
|
|
stds.sort(key=lambda x: x[1], reverse=True)
|
|||
|
|
for i, _ in stds:
|
|||
|
|
if i not in chosen_idx:
|
|||
|
|
chosen_idx.append(i)
|
|||
|
|
if len(chosen_idx) == 3:
|
|||
|
|
break
|
|||
|
|
chosen_name = [ch_names[i] for i in chosen_idx]
|
|||
|
|
else:
|
|||
|
|
stds = [(i, float(np.std(eeg_uV_tc[:, i]))) for i in range(C)]
|
|||
|
|
stds.sort(key=lambda x: x[1], reverse=True)
|
|||
|
|
chosen_idx = [i for i, _ in stds[:3]]
|
|||
|
|
chosen_name = [f"CH{i}" for i in chosen_idx]
|
|||
|
|
|
|||
|
|
fig = plt.figure(figsize=(7.5, 4.8))
|
|||
|
|
for idx, nm in zip(chosen_idx, chosen_name):
|
|||
|
|
f, pxx = signal.welch(eeg_uV_tc[:, idx], fs=fs, nperseg=int(2*fs), noverlap=int(1*fs))
|
|||
|
|
mask = (f >= PSD_FMIN) & (f <= PSD_FMAX)
|
|||
|
|
p_db = 10 * np.log10(pxx[mask] + 1e-20)
|
|||
|
|
plt.plot(f[mask], p_db, linewidth=1.8, label=nm)
|
|||
|
|
|
|||
|
|
plt.xlabel("Hz")
|
|||
|
|
plt.ylabel("Power (dB)")
|
|||
|
|
plt.title("PSD")
|
|||
|
|
plt.grid(True, alpha=0.3)
|
|||
|
|
plt.legend()
|
|||
|
|
plt.tight_layout()
|
|||
|
|
out_path = os.path.join(out_dir, "psd.png")
|
|||
|
|
plt.savefig(out_path, dpi=200)
|
|||
|
|
plt.close(fig)
|
|||
|
|
print(f"[OK] psd.png -> {out_path}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ==========================
|
|||
|
|
# Topomap（如果有 xyz）
|
|||
|
|
# ==========================
|
|||
|
|
def build_mne_raw_from_uV(eeg_uV_tc, fs, ch_names, xyz):
|
|||
|
|
C = eeg_uV_tc.shape[1]
|
|||
|
|
if not ch_names:
|
|||
|
|
ch_names = [f"CH{i+1}" for i in range(C)]
|
|||
|
|
data_v_ct = eeg_uV_tc.T * 1e-6 # (C,T) V
|
|||
|
|
info = mne.create_info(ch_names=ch_names, sfreq=fs, ch_types=["eeg"] * C)
|
|||
|
|
raw = mne.io.RawArray(data_v_ct, info, verbose=False)
|
|||
|
|
|
|||
|
|
if xyz is not None and isinstance(xyz, np.ndarray) and xyz.shape == (C, 3):
|
|||
|
|
try:
|
|||
|
|
ch_pos = {ch_names[i]: xyz[i, :] for i in range(C)}
|
|||
|
|
montage = mne.channels.make_dig_montage(ch_pos=ch_pos, coord_frame="head")
|
|||
|
|
raw.set_montage(montage, on_missing="ignore")
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"[WARN] set_montage failed (ignore): {e}")
|
|||
|
|
else:
|
|||
|
|
print("[WARN] electrode_xyz missing/invalid -> skip topomap")
|
|||
|
|
|
|||
|
|
return raw
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _raw_has_positions(raw):
|
|||
|
|
try:
|
|||
|
|
locs = np.array([ch["loc"][:3] for ch in raw.info["chs"]])
|
|||
|
|
ok = np.isfinite(locs).all() and (np.linalg.norm(locs, axis=1) > 0).any()
|
|||
|
|
return bool(ok)
|
|||
|
|
except Exception:
|
|||
|
|
return False
|
|||
|
|
|
|||
|
|
|
|||
|
|
def compute_band_powers_for_topomap(raw, bands):
|
|||
|
|
data = raw.get_data() # (C,T) V
|
|||
|
|
fs = raw.info["sfreq"]
|
|||
|
|
psds, freqs = mne.time_frequency.psd_array_welch(
|
|||
|
|
data, sfreq=fs,
|
|||
|
|
fmin=min(v[0] for v in bands.values()),
|
|||
|
|
fmax=max(v[1] for v in bands.values()),
|
|||
|
|
n_fft=int(2 * fs),
|
|||
|
|
n_overlap=int(1 * fs),
|
|||
|
|
average="mean",
|
|||
|
|
verbose=False
|
|||
|
|
)
|
|||
|
|
out = {}
|
|||
|
|
for k, (fmin, fmax) in bands.items():
|
|||
|
|
idx = np.where((freqs >= fmin) & (freqs <= fmax))[0]
|
|||
|
|
|
|||
|
|
# 兼容处理：numpy 2.0+ 推荐使用 trapezoid，旧版本用 trapz
|
|||
|
|
if hasattr(np, "trapezoid"):
|
|||
|
|
bp = np.trapezoid(psds[:, idx], freqs[idx], axis=1) # (C,)
|
|||
|
|
else:
|
|||
|
|
bp = np.trapz(psds[:, idx], freqs[idx], axis=1) # (C,)
|
|||
|
|
|
|||
|
|
v = np.log10(bp + 1e-30)
|
|||
|
|
v = v - np.mean(v)
|
|||
|
|
out[k] = v
|
|||
|
|
return out
|
|||
|
|
|
|||
|
|
|
|||
|
|
def plot_average_topomap(raw, values, out_dir):
|
|||
|
|
fig, ax = plt.subplots(1, 1, figsize=(6.5, 4.6))
|
|||
|
|
im, _ = mne.viz.plot_topomap(values, raw.info, axes=ax, show=False, contours=0,sphere=(0, 0, 0, 0.11))
|
|||
|
|
ax.set_title("0.8-30 Hz", fontsize=12)
|
|||
|
|
plt.colorbar(im, ax=ax, shrink=0.85)
|
|||
|
|
plt.tight_layout()
|
|||
|
|
out_path = os.path.join(out_dir, "average_topomap.png")
|
|||
|
|
plt.savefig(out_path, dpi=200)
|
|||
|
|
plt.close(fig)
|
|||
|
|
print(f"[OK] average_topomap.png -> {out_path}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def plot_band_topomaps(raw, band_values, out_dir):
|
|||
|
|
order = [
|
|||
|
|
("delta", "δ (0.8-3.9Hz)"),
|
|||
|
|
("theta", "θ (4-7.9Hz)"),
|
|||
|
|
("alpha", "α (8-12.9Hz)"),
|
|||
|
|
("beta", "β (13-30Hz)"),
|
|||
|
|
("broad", "0.8-30 Hz"),
|
|||
|
|
]
|
|||
|
|
fig, axes = plt.subplots(1, 5, figsize=(16, 4.2))
|
|||
|
|
ims = []
|
|||
|
|
for ax, (k, title) in zip(axes, order):
|
|||
|
|
im, _ = mne.viz.plot_topomap(band_values[k], raw.info, axes=ax, show=False, contours=0,extrapolate='head',sphere=(0, 0, 0, 0.11))
|
|||
|
|
ax.set_title(title, fontsize=11)
|
|||
|
|
ims.append(im)
|
|||
|
|
fig.subplots_adjust(left=0.02, right=0.85, top=0.88, bottom=0.05, wspace=0.35)
|
|||
|
|
cax = fig.add_axes([0.87, 0.15, 0.015, 0.7])
|
|||
|
|
fig.colorbar(ims[-1], cax=cax)
|
|||
|
|
out_path = os.path.join(out_dir, "topomaps.png")
|
|||
|
|
plt.savefig(out_path, dpi=200)
|
|||
|
|
plt.close(fig)
|
|||
|
|
print(f"[OK] topomaps.png -> {out_path}")
|
|||
|
|
|
|||
|
|
# ==========================
|
|||
|
|
# 生成 ResultData.txt
|
|||
|
|
# ==========================
|
|||
|
|
def compute_and_save_txt(model_path, mat_dir, out_dir, eeg_uV_tc, fs, ch_names):
|
|||
|
|
pred_label = _predict_label_by_model(model_path, mat_dir)
|
|||
|
|
recommend = "是" if pred_label == "MDD" else "否"
|
|||
|
|
|
|||
|
|
T, C = eeg_uV_tc.shape
|
|||
|
|
mp = build_channel_index_map(ch_names, C)
|
|||
|
|
frontal_idx, central_idx, parietal_idx, prefrontal_idx, posterior_idx, left_idx, right_idx = \
|
|||
|
|
get_region_indices(mp, C)
|
|||
|
|
|
|||
|
|
freqs, pxx = welch_psd(eeg_uV_tc, fs)
|
|||
|
|
|
|||
|
|
central_alpha = region_mean_power(freqs, pxx, central_idx, BANDS_METRICS["Alpha"])
|
|||
|
|
central_beta = region_mean_power(freqs, pxx, central_idx, BANDS_METRICS["Beta"])
|
|||
|
|
frontal_alpha = region_mean_power(freqs, pxx, frontal_idx, BANDS_METRICS["Alpha"])
|
|||
|
|
frontal_beta = region_mean_power(freqs, pxx, frontal_idx, BANDS_METRICS["Beta"])
|
|||
|
|
par_alpha = region_mean_power(freqs, pxx, parietal_idx, BANDS_METRICS["Alpha"])
|
|||
|
|
par_beta = region_mean_power(freqs, pxx, parietal_idx, BANDS_METRICS["Beta"])
|
|||
|
|
|
|||
|
|
central_ab = (central_alpha / (central_beta + EPS)) if central_beta > 0 else 0.0
|
|||
|
|
frontal_ab = (frontal_alpha / (frontal_beta + EPS)) if frontal_beta > 0 else 0.0
|
|||
|
|
par_ab = (par_alpha / (par_beta + EPS)) if par_beta > 0 else 0.0
|
|||
|
|
|
|||
|
|
central_theta = region_mean_power(freqs, pxx, central_idx, BANDS_METRICS["Theta"])
|
|||
|
|
par_theta = region_mean_power(freqs, pxx, parietal_idx, BANDS_METRICS["Theta"])
|
|||
|
|
central_tb = (central_theta / (central_beta + EPS)) if central_beta > 0 else 0.0
|
|||
|
|
par_tb = (par_theta / (par_beta + EPS)) if par_beta > 0 else 0.0
|
|||
|
|
|
|||
|
|
if not left_idx or not right_idx:
|
|||
|
|
left_idx = [i for i in prefrontal_idx if (i % 2 == 0)]
|
|||
|
|
right_idx = [i for i in prefrontal_idx if (i % 2 == 1)]
|
|||
|
|
left_alpha = region_mean_power(freqs, pxx, left_idx, BANDS_METRICS["Alpha"])
|
|||
|
|
right_alpha = region_mean_power(freqs, pxx, right_idx, BANDS_METRICS["Alpha"])
|
|||
|
|
prefrontal_alpha_asym = float(np.log(right_alpha + EPS) - np.log(left_alpha + EPS))
|
|||
|
|
|
|||
|
|
iaf = compute_iaf(freqs, pxx, posterior_idx)
|
|||
|
|
|
|||
|
|
pre_td = region_mean_power(freqs, pxx, prefrontal_idx, (BANDS_METRICS["Delta"][0], BANDS_METRICS["Theta"][1]))
|
|||
|
|
pre_total = region_mean_power(freqs, pxx, prefrontal_idx, TOTAL_POWER_BAND)
|
|||
|
|
pre_td_rel = (pre_td / (pre_total + EPS)) * 100.0 if pre_total > 0 else 0.0
|
|||
|
|
|
|||
|
|
def f1(x): return f"{x:.1f}"
|
|||
|
|
|
|||
|
|
txt = (
|
|||
|
|
f"中央区α/β波比值:{f1(central_ab)}\n"
|
|||
|
|
f"额区α/β波比值:{f1(frontal_ab)}\n"
|
|||
|
|
f"顶区α/β波比值:{f1(par_ab)}\n"
|
|||
|
|
f"中央区θ/β波比值:{f1(central_tb)}\n"
|
|||
|
|
f"顶区θ/β波比值:{f1(par_tb)}\n"
|
|||
|
|
f"前额叶α波不对称性:{f1(prefrontal_alpha_asym)}\n"
|
|||
|
|
f"个体化α峰值频率:{f1(iaf)}\n"
|
|||
|
|
f"前额叶θ+δ波功率:{f1(pre_td_rel)}\n"
|
|||
|
|
f"是否推荐治疗:{recommend}\n"
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
out_path = os.path.join(out_dir, "ResultData.txt")
|
|||
|
|
with open(out_path, "w", encoding="utf-8") as f:
|
|||
|
|
f.write(txt)
|
|||
|
|
print(f"[OK] ResultData.txt -> {out_path}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ==========================
|
|||
|
|
# 一个函数：一次性跑完（txt + 图片）
|
|||
|
|
# ==========================
|
|||
|
|
def run_all(model_path: str, mat_dir: str, out_root: str, seconds: int = EEG_PLOT_SECONDS):
|
|||
|
|
# 1) 选第一个 mat
|
|||
|
|
if not os.path.exists(mat_dir):
|
|||
|
|
raise RuntimeError(f"输入目录不存在: {mat_dir}")
|
|||
|
|
|
|||
|
|
mats = [f for f in os.listdir(mat_dir) if f.lower().endswith(".mat")]
|
|||
|
|
if not mats:
|
|||
|
|
raise RuntimeError(f"mat_dir 下找不到 .mat: {mat_dir}")
|
|||
|
|
mats.sort()
|
|||
|
|
mat_file = os.path.join(mat_dir, mats[0])
|
|||
|
|
print(f"[INFO] Found mat: {mat_file}")
|
|||
|
|
|
|||
|
|
# 2) 创建输出目录
|
|||
|
|
out_dir = ensure_outdir(out_root)
|
|||
|
|
print(f"[INFO] Output dir: {out_dir}")
|
|||
|
|
|
|||
|
|
# --- 总是进行预处理 (默认模式) ---
|
|||
|
|
print("[INFO] Mode: Raw Data (Default). Running preprocessing...")
|
|||
|
|
temp_dir = os.path.join(out_dir, "temp_preprocessed")
|
|||
|
|
mat_file = preprocess_mat_file(mat_file, temp_dir)
|
|||
|
|
# 更新 mat_dir 指向临时目录（为了传给 compute_and_save_txt 里的 predict 接口）
|
|||
|
|
mat_dir = temp_dir
|
|||
|
|
|
|||
|
|
# 3) 读 EEG（μV）
|
|||
|
|
eeg_uV_tc, fs, ch_names, xyz = load_eeg_from_mat(mat_file)
|
|||
|
|
print(f"[INFO] eeg shape(T,C)={eeg_uV_tc.shape}, fs={fs}")
|
|||
|
|
|
|||
|
|
# 5) 画图：PSD + EEG
|
|||
|
|
plot_psd(eeg_uV_tc, fs, ch_names, out_dir)
|
|||
|
|
plot_eeg_waveforms(eeg_uV_tc, fs, ch_names, out_dir, seconds=seconds)
|
|||
|
|
|
|||
|
|
# 6) topomap（有 xyz 才画）
|
|||
|
|
try:
|
|||
|
|
raw = build_mne_raw_from_uV(eeg_uV_tc, fs, ch_names, xyz)
|
|||
|
|
if _raw_has_positions(raw):
|
|||
|
|
band_vals = compute_band_powers_for_topomap(raw, BANDS_TOPOMAP)
|
|||
|
|
plot_average_topomap(raw, band_vals["broad"], out_dir)
|
|||
|
|
plot_band_topomaps(raw, band_vals, out_dir)
|
|||
|
|
else:
|
|||
|
|
print("[WARN] No valid positions -> skip topomap.")
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"[WARN] topomap failed -> skip. reason: {e}")
|
|||
|
|
|
|||
|
|
# 4) 指标写 txt
|
|||
|
|
compute_and_save_txt(model_path, mat_dir, out_dir, eeg_uV_tc, fs, ch_names)
|
|||
|
|
|
|||
|
|
print("[DONE] txt + figures generated.")
|
|||
|
|
return out_dir
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
import multiprocessing
|
|||
|
|
multiprocessing.freeze_support()
|
|||
|
|
import argparse
|
|||
|
|
import sys
|
|||
|
|
|
|||
|
|
# 1. 路径锚定：获取资源绝对路径
|
|||
|
|
def get_resource_path(relative_path):
|
|||
|
|
"""
|
|||
|
|
获取资源的绝对路径。
|
|||
|
|
策略：优先在当前执行目录（EXE所在目录）寻找。
|
|||
|
|
这适用于“绿色软件”模式，即资源文件（model/raw_data）直接放在EXE旁边。
|
|||
|
|
"""
|
|||
|
|
if getattr(sys, 'frozen', False):
|
|||
|
|
# PyInstaller 打包后的 EXE 所在目录
|
|||
|
|
base_path = os.path.dirname(sys.executable)
|
|||
|
|
else:
|
|||
|
|
# 开发环境：当前脚本所在目录
|
|||
|
|
base_path = os.path.dirname(os.path.abspath(__file__))
|
|||
|
|
|
|||
|
|
return os.path.join(base_path, relative_path)
|
|||
|
|
|
|||
|
|
# 设置默认路径
|
|||
|
|
DEFAULT_MODEL = get_resource_path(os.path.join("model", "Model_1.pth"))
|
|||
|
|
# 这里我们保持 mat_dir 和 out_root 相对于 EXE 所在目录（或当前工作目录）
|
|||
|
|
if getattr(sys, 'frozen', False):
|
|||
|
|
EXE_DIR = os.path.dirname(sys.executable)
|
|||
|
|
else:
|
|||
|
|
EXE_DIR = os.path.dirname(os.path.abspath(__file__))
|
|||
|
|
|
|||
|
|
DEFAULT_MAT = os.path.join(EXE_DIR, "raw_data")
|
|||
|
|
DEFAULT_OUT = os.path.join(EXE_DIR, "out")
|
|||
|
|
|
|||
|
|
# 2. 解析命令行参数
|
|||
|
|
parser = argparse.ArgumentParser(description="EEG Depression Assessment Algorithm Integration")
|
|||
|
|
parser.add_argument("--model_path", type=str, default=DEFAULT_MODEL, help="模型文件的路径 (.pth)")
|
|||
|
|
parser.add_argument("--mat_dir", type=str, default=DEFAULT_MAT, help="输入文件夹路径 (包含原始EEG .mat)")
|
|||
|
|
parser.add_argument("--out_root", type=str, default=DEFAULT_OUT, help="结果输出的根目录")
|
|||
|
|
parser.add_argument("--seconds", type=int, default=10, help="画波形图截取的秒数")
|
|||
|
|
|
|||
|
|
args = parser.parse_args()
|
|||
|
|
|
|||
|
|
# 3. 检查关键路径
|
|||
|
|
if not os.path.exists(args.mat_dir):
|
|||
|
|
print(f"[WARN] 输入文件夹不存在: {args.mat_dir}")
|
|||
|
|
if not os.path.exists(args.model_path):
|
|||
|
|
print(f"[WARN] 模型文件不存在: {args.model_path}")
|
|||
|
|
|
|||
|
|
# 4. 执行主流程
|
|||
|
|
print(f"[*] 运行配置:")
|
|||
|
|
print(f" - Model : {args.model_path}")
|
|||
|
|
print(f" - Input : {args.mat_dir}")
|
|||
|
|
print(f" - Output: {args.out_root}")
|
|||
|
|
print(f" - Mode : RAW (Auto Preprocess)")
|
|||
|
|
|
|||
|
|
run_all(args.model_path, args.mat_dir, args.out_root, seconds=args.seconds)
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|