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Ivey Song
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algorithm_V1/bdf_to_mat.py
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203
algorithm_V1/bdf_to_mat.py
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# -*- coding: utf-8 -*-
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"""
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Convert BDF file to MAT format.
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This script converts a BDF (Biosemi Data Format) EEG file to .mat format,
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matching the structure of eeg_data.mat.
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Structure of eeg_data.mat:
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- data: (n_samples, n_channels) float64
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- chn: (1, n_channels) object - channel names
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- sample_rate: (1, 1) int64
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- node_number: (1, 1) int64
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- t: (n_samples, 1) float64 - time vector in seconds
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- electrode_name: (1, n_channels) object - electrode names (10-20 system)
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- electrode_xyz: (n_channels, 3) float64 - electrode 3D coordinates
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- electrode_coord_system: (1,) <U21
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- meta: (1, 1) structured - metadata
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"""
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import numpy as np
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import scipy.io
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import mne
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from datetime import datetime
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def get_standard_electrode_coords():
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"""
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Standard 10-20 system electrode coordinates.
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Returns a dictionary mapping electrode names to x, y, z coordinates.
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Coordinates are approximate spherical projections.
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"""
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coords = {
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'FP1': (-0.0293, 0.0903, -0.0033),
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'FP2': (0.0293, 0.0903, -0.0033),
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'FPZ': (0.0, 0.0903, -0.0033),
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'AF7': (-0.0658, 0.0734, -0.0224),
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'AF3': (-0.0350, 0.0812, -0.0183),
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'AF4': (0.0350, 0.0812, -0.0183),
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'AF8': (0.0658, 0.0734, -0.0224),
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'F7': (-0.0815, 0.0467, -0.0336),
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'F5': (-0.0667, 0.0503, -0.0351),
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'F3': (-0.0489, 0.0560, -0.0370),
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'F1': (-0.0254, 0.0584, -0.0384),
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'FZ': (0.0, 0.0584, -0.0384),
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'F2': (0.0254, 0.0584, -0.0384),
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'F4': (0.0489, 0.0560, -0.0370),
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'F6': (0.0667, 0.0503, -0.0351),
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'F8': (0.0815, 0.0467, -0.0336),
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'FT7': (-0.0880, 0.0229, -0.0397),
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'FC5': (-0.0699, 0.0317, -0.0402),
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'FC3': (-0.0514, 0.0362, -0.0411),
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'FC1': (-0.0268, 0.0383, -0.0419),
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'FCZ': (0.0, 0.0383, -0.0419),
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'FC2': (0.0268, 0.0383, -0.0419),
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'FC4': (0.0514, 0.0362, -0.0411),
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'FC6': (0.0699, 0.0317, -0.0402),
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'FT8': (0.0880, 0.0229, -0.0397),
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'T7': (-0.0958, 0.0, -0.0411),
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'T8': (0.0958, 0.0, -0.0411),
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'C5': (-0.0739, 0.0, -0.0425),
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'C3': (-0.0544, 0.0, -0.0436),
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'C1': (-0.0283, 0.0, -0.0444),
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'CZ': (0.0, 0.0, -0.0444),
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'C2': (0.0283, 0.0, -0.0444),
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'C4': (0.0544, 0.0, -0.0436),
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'C6': (0.0739, 0.0, -0.0425),
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'TP7': (-0.0880, -0.0229, -0.0397),
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'CP5': (-0.0699, -0.0317, -0.0402),
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'CP3': (-0.0514, -0.0362, -0.0411),
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'CP1': (-0.0268, -0.0383, -0.0419),
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'CPZ': (0.0, -0.0383, -0.0419),
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'CP2': (0.0268, -0.0383, -0.0419),
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'CP4': (0.0514, -0.0362, -0.0411),
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'CP6': (0.0699, -0.0317, -0.0402),
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'TP8': (0.0880, -0.0229, -0.0397),
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'P7': (-0.0815, -0.0467, -0.0336),
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'P5': (-0.0667, -0.0503, -0.0351),
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'P3': (-0.0489, -0.0560, -0.0370),
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'P1': (-0.0254, -0.0584, -0.0384),
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'PZ': (0.0, -0.0584, -0.0384),
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'P2': (0.0254, -0.0584, -0.0384),
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'P4': (0.0489, -0.0560, -0.0370),
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'P6': (0.0667, -0.0503, -0.0351),
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'P8': (0.0815, -0.0467, -0.0336),
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'PO7': (-0.0658, -0.0734, -0.0224),
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'PO5': (-0.0503, -0.0744, -0.0258),
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'PO3': (-0.0350, -0.0812, -0.0183),
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'POZ': (0.0, -0.0829, -0.0172),
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'PO4': (0.0350, -0.0812, -0.0183),
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'PO6': (0.0503, -0.0744, -0.0258),
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'PO8': (0.0658, -0.0734, -0.0224),
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'O1': (-0.0293, -0.0903, -0.0033),
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'OZ': (0.0, -0.0903, -0.0033),
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'O2': (0.0293, -0.0903, -0.0033),
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'CB1': (-0.0618, -0.0380, -0.0387),
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'CB2': (0.0618, -0.0380, -0.0387),
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'A1': (-0.0958, 0.0, 0.0),
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'A2': (0.0958, 0.0, 0.0),
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}
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return coords
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def bdf_to_mat(bdf_path, output_path, subject_id='unknown', session_id='unknown'):
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"""
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Convert BDF file to MAT format matching eeg_data.mat structure.
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Parameters
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----------
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bdf_path : str
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Path to the input BDF file.
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output_path : str
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Path to the output MAT file.
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subject_id : str, optional
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Subject identifier. Default is 'unknown'.
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session_id : str, optional
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Session identifier. Default is 'unknown'.
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"""
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print(f'Loading BDF file: {bdf_path}')
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raw = mne.io.read_raw_bdf(bdf_path, preload=True, verbose=False)
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# Get basic info
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ch_names = raw.ch_names
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n_channels = len(ch_names)
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sfreq = int(raw.info['sfreq'])
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data = raw.get_data()
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# BDF data shape: (n_channels, n_samples)
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# Convert to eeg_data.mat format: (n_samples, n_channels)
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data = data.T
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n_samples = data.shape[0]
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# Create time vector (in seconds)
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t = np.arange(n_samples) / sfreq
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t = t.reshape(-1, 1)
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# Create channel names array (matching eeg_data.mat structure)
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chn = np.array([[name] for name in ch_names], dtype=object)
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# Create electrode names (same as channel names for BDF)
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electrode_name = np.array([[name] for name in ch_names], dtype=object)
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# Get electrode coordinates
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standard_coords = get_standard_electrode_coords()
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electrode_xyz = np.zeros((n_channels, 3))
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for i, name in enumerate(ch_names):
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if name in standard_coords:
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electrode_xyz[i] = standard_coords[name]
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else:
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print(f'Warning: No standard coordinate for electrode {name}')
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# Create metadata structure
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start_time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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meta = np.array([(subject_id, session_id, 'CMS/DRL', start_time_str)],
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dtype=[('subject_id', 'O'), ('session_id', 'O'),
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('ref', 'O'), ('start_time', 'O')])
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# Create the EEG structure (matching eeg_data.mat format)
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eeg_struct = np.array([(data, chn, [[sfreq]], [[n_channels]],
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t, electrode_name, electrode_xyz,
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'buzsaki', meta)],
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dtype=[('data', 'O'), ('chn', 'O'),
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('sample_rate', 'O'), ('node_number', 'O'),
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('t', 'O'), ('electrode_name', 'O'),
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('electrode_xyz', 'O'), ('electrode_coord_system', 'O'),
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('meta', 'O')])
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# Save to MAT file
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print(f'Saving to: {output_path}')
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scipy.io.savemat(output_path, {'eeg': eeg_struct}, do_compression=True)
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print(f'\nConversion complete!')
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print(f' Channels: {n_channels}')
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print(f' Samples: {n_samples}')
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print(f' Duration: {n_samples / sfreq:.2f} seconds')
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print(f' Sample rate: {sfreq} Hz')
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print(f' Data shape: {data.shape}')
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def main():
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# File paths
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bdf_path = r'D:\Ivey\Code_New_Proj\Debug_Depression\algorithm_version_0521_v0\0515-18.bdf'
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output_path = r'D:\Ivey\Code_New_Proj\Debug_Depression\algorithm_version_0521_v0\0515-18.mat'
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# Convert
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bdf_to_mat(bdf_path, output_path, subject_id='lvpeng', session_id='01')
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# Verify the output
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print('\n=== Verification ===')
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mat_data = scipy.io.loadmat(output_path)
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eeg = mat_data['eeg'][0, 0]
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print(f'Output file keys: {list(mat_data.keys())}')
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print(f'eeg.data shape: {eeg["data"].shape}')
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print(f'eeg.chn shape: {eeg["chn"].shape}')
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print(f'eeg.sample_rate: {eeg["sample_rate"][0, 0]}')
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print(f'eeg.t shape: {eeg["t"].shape}')
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print(f'eeg.electrode_name: {eeg["electrode_name"].shape}')
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print(f'eeg.electrode_xyz shape: {eeg["electrode_xyz"].shape}')
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print(f'eeg.electrode_coord_system: {eeg["electrode_coord_system"][0]}')
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if __name__ == '__main__':
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main()
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