| import os |
| import random |
|
|
| import mne |
| import numpy as np |
| from tqdm import tqdm |
| import pickle |
| import lmdb |
|
|
|
|
| selected_channels = { |
| '01_tcp_ar': [ |
| 'EEG FP1-REF', 'EEG FP2-REF', 'EEG F3-REF', 'EEG F4-REF', 'EEG C3-REF', 'EEG C4-REF', 'EEG P3-REF', |
| 'EEG P4-REF', 'EEG O1-REF', 'EEG O2-REF', 'EEG F7-REF', 'EEG F8-REF', 'EEG T3-REF', 'EEG T4-REF', |
| 'EEG T5-REF', 'EEG T6-REF', 'EEG FZ-REF', 'EEG CZ-REF', 'EEG PZ-REF' |
| ], |
| '02_tcp_le': [ |
| 'EEG FP1-LE', 'EEG FP2-LE', 'EEG F3-LE', 'EEG F4-LE', 'EEG C3-LE', 'EEG C4-LE', 'EEG P3-LE', |
| 'EEG P4-LE', 'EEG O1-LE', 'EEG O2-LE', 'EEG F7-LE', 'EEG F8-LE', 'EEG T3-LE', 'EEG T4-LE', |
| 'EEG T5-LE', 'EEG T6-LE', 'EEG FZ-LE', 'EEG CZ-LE', 'EEG PZ-LE' |
| ], |
| '03_tcp_ar_a': [ |
| 'EEG FP1-REF', 'EEG FP2-REF', 'EEG F3-REF', 'EEG F4-REF', 'EEG C3-REF', 'EEG C4-REF', 'EEG P3-REF', |
| 'EEG P4-REF', 'EEG O1-REF', 'EEG O2-REF', 'EEG F7-REF', 'EEG F8-REF', 'EEG T3-REF', 'EEG T4-REF', |
| 'EEG T5-REF', 'EEG T6-REF', 'EEG FZ-REF', 'EEG CZ-REF', 'EEG PZ-REF' |
| ] |
| } |
|
|
| def setup_seed(seed): |
| np.random.seed(seed) |
| random.seed(seed) |
|
|
|
|
| |
| def iter_files(rootDir): |
| |
| file_path_list = [] |
| for root,dirs,files in os.walk(rootDir): |
| for file in files: |
| file_name = os.path.join(root,file) |
| |
| file_path_list.append(file_name) |
| return file_path_list |
|
|
| def preprocessing_recording(file_path, file_key_list: list, db: lmdb.open): |
| raw = mne.io.read_raw_edf(file_path, preload=True) |
| if '02_tcp_le' in file_path: |
| for ch in selected_channels['02_tcp_le']: |
| if ch not in raw.info['ch_names']: |
| return |
| raw.pick_channels(selected_channels['02_tcp_le'], ordered=True) |
| elif '01_tcp_ar' in file_path: |
| for ch in selected_channels['01_tcp_ar']: |
| if ch not in raw.info['ch_names']: |
| return |
| raw.pick_channels(selected_channels['01_tcp_ar'], ordered=True) |
| elif '03_tcp_ar_a' in file_path: |
| for ch in selected_channels['03_tcp_ar_a']: |
| if ch not in raw.info['ch_names']: |
| return |
| raw.pick_channels(selected_channels['03_tcp_ar_a'], ordered=True) |
| else: |
| return |
| |
| raw.resample(200) |
| raw.filter(l_freq=0.3, h_freq=75) |
| raw.notch_filter((60)) |
| eeg_array = raw.to_data_frame().values |
| |
| eeg_array = eeg_array[:, 1:] |
| points, chs = eeg_array.shape |
| if points < 300 * 200: |
| return |
| a = points % (30 * 200) |
| eeg_array = eeg_array[60 * 200:-(a+60 * 200), :] |
| |
| eeg_array = eeg_array.reshape(-1, 30, 200, chs) |
| eeg_array = eeg_array.transpose(0, 3, 1, 2) |
| print(eeg_array.shape) |
| file_name = file_path.split('/')[-1][:-4] |
|
|
| for i, sample in enumerate(eeg_array): |
| |
| if np.max(np.abs(sample)) < 100: |
| sample_key = f'{file_name}_{i}' |
| print(sample_key) |
| file_key_list.append(sample_key) |
| txn = db.begin(write=True) |
| txn.put(key=sample_key.encode(), value=pickle.dumps(sample)) |
| txn.commit() |
|
|
| if __name__ == '__main__': |
| setup_seed(1) |
| file_path_list = iter_files('path...') |
|
|
| file_path_list = sorted(file_path_list) |
| random.shuffle(file_path_list) |
| |
| db = lmdb.open(r'path...', map_size=1649267441664) |
| file_key_list = [] |
| for file_path in tqdm(file_path_list): |
| preprocessing_recording(file_path, file_key_list, db) |
|
|
| txn = db.begin(write=True) |
| txn.put(key='__keys__'.encode(), value=pickle.dumps(file_key_list)) |
| txn.commit() |
| db.close() |
|
|
