import os import pickle from multiprocessing import Pool import numpy as np import mne # we need these channels # (signals[signal_names['EEG FP1-REF']] - signals[signal_names['EEG F7-REF']], # 0 # (signals[signal_names['EEG F7-REF']] - signals[signal_names['EEG T3-REF']]), # 1 # (signals[signal_names['EEG T3-REF']] - signals[signal_names['EEG T5-REF']]), # 2 # (signals[signal_names['EEG T5-REF']] - signals[signal_names['EEG O1-REF']]), # 3 # (signals[signal_names['EEG FP2-REF']] - signals[signal_names['EEG F8-REF']]), # 4 # (signals[signal_names['EEG F8-REF']] - signals[signal_names['EEG T4-REF']]), # 5 # (signals[signal_names['EEG T4-REF']] - signals[signal_names['EEG T6-REF']]), # 6 # (signals[signal_names['EEG T6-REF']] - signals[signal_names['EEG O2-REF']]), # 7 # (signals[signal_names['EEG FP1-REF']] - signals[signal_names['EEG F3-REF']]), # 14 # (signals[signal_names['EEG F3-REF']] - signals[signal_names['EEG C3-REF']]), # 15 # (signals[signal_names['EEG C3-REF']] - signals[signal_names['EEG P3-REF']]), # 16 # (signals[signal_names['EEG P3-REF']] - signals[signal_names['EEG O1-REF']]), # 17 # (signals[signal_names['EEG FP2-REF']] - signals[signal_names['EEG F4-REF']]), # 18 # (signals[signal_names['EEG F4-REF']] - signals[signal_names['EEG C4-REF']]), # 19 # (signals[signal_names['EEG C4-REF']] - signals[signal_names['EEG P4-REF']]), # 20 # (signals[signal_names['EEG P4-REF']] - signals[signal_names['EEG O2-REF']]))) # 21 standard_channels = [ "EEG FP1-REF", "EEG F7-REF", "EEG T3-REF", "EEG T5-REF", "EEG O1-REF", "EEG FP2-REF", "EEG F8-REF", "EEG T4-REF", "EEG T6-REF", "EEG O2-REF", "EEG FP1-REF", "EEG F3-REF", "EEG C3-REF", "EEG P3-REF", "EEG O1-REF", "EEG FP2-REF", "EEG F4-REF", "EEG C4-REF", "EEG P4-REF", "EEG O2-REF", ] def split_and_dump(params): fetch_folder, sub, dump_folder, label = params for file in os.listdir(fetch_folder): if sub in file: print("process", file) file_path = os.path.join(fetch_folder, file) raw = mne.io.read_raw_edf(file_path, preload=True) raw.resample(200) raw.filter(l_freq=0.3, h_freq=75) raw.notch_filter((60)) ch_name = raw.ch_names raw_data = raw.get_data(units='uV') channeled_data = raw_data.copy()[:16] try: channeled_data[0] = ( raw_data[ch_name.index("EEG FP1-REF")] - raw_data[ch_name.index("EEG F7-REF")] ) channeled_data[1] = ( raw_data[ch_name.index("EEG F7-REF")] - raw_data[ch_name.index("EEG T3-REF")] ) channeled_data[2] = ( raw_data[ch_name.index("EEG T3-REF")] - raw_data[ch_name.index("EEG T5-REF")] ) channeled_data[3] = ( raw_data[ch_name.index("EEG T5-REF")] - raw_data[ch_name.index("EEG O1-REF")] ) channeled_data[4] = ( raw_data[ch_name.index("EEG FP2-REF")] - raw_data[ch_name.index("EEG F8-REF")] ) channeled_data[5] = ( raw_data[ch_name.index("EEG F8-REF")] - raw_data[ch_name.index("EEG T4-REF")] ) channeled_data[6] = ( raw_data[ch_name.index("EEG T4-REF")] - raw_data[ch_name.index("EEG T6-REF")] ) channeled_data[7] = ( raw_data[ch_name.index("EEG T6-REF")] - raw_data[ch_name.index("EEG O2-REF")] ) channeled_data[8] = ( raw_data[ch_name.index("EEG FP1-REF")] - raw_data[ch_name.index("EEG F3-REF")] ) channeled_data[9] = ( raw_data[ch_name.index("EEG F3-REF")] - raw_data[ch_name.index("EEG C3-REF")] ) channeled_data[10] = ( raw_data[ch_name.index("EEG C3-REF")] - raw_data[ch_name.index("EEG P3-REF")] ) channeled_data[11] = ( raw_data[ch_name.index("EEG P3-REF")] - raw_data[ch_name.index("EEG O1-REF")] ) channeled_data[12] = ( raw_data[ch_name.index("EEG FP2-REF")] - raw_data[ch_name.index("EEG F4-REF")] ) channeled_data[13] = ( raw_data[ch_name.index("EEG F4-REF")] - raw_data[ch_name.index("EEG C4-REF")] ) channeled_data[14] = ( raw_data[ch_name.index("EEG C4-REF")] - raw_data[ch_name.index("EEG P4-REF")] ) channeled_data[15] = ( raw_data[ch_name.index("EEG P4-REF")] - raw_data[ch_name.index("EEG O2-REF")] ) except: with open("tuab-process-error-files.txt", "a") as f: f.write(file + "\n") continue for i in range(channeled_data.shape[1] // 2000): dump_path = os.path.join( dump_folder, file.split(".")[0] + "_" + str(i) + ".pkl" ) pickle.dump( {"X": channeled_data[:, i * 2000 : (i + 1) * 2000], "y": label}, open(dump_path, "wb"), ) if __name__ == "__main__": """ TUAB dataset is downloaded from https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml """ # root to abnormal dataset root = "/data/datasets/BigDownstream/TUAB/edf" channel_std = "01_tcp_ar" # seed = 4523 # np.random.seed(seed) # train, val abnormal subjects train_val_abnormal = os.path.join(root, "train", "abnormal", channel_std) train_val_a_sub = list( set([item.split("_")[0] for item in os.listdir(train_val_abnormal)]) ) train_val_a_sub.sort(key=lambda x: x) train_a_sub, val_a_sub = ( train_val_a_sub[: int(len(train_val_a_sub) * 0.8)], train_val_a_sub[int(len(train_val_a_sub) * 0.8) :], ) print('train_a_sub:', train_a_sub) print('val_a_sub:', val_a_sub) # train, val normal subjects train_val_normal = os.path.join(root, "train", "normal", channel_std) train_val_n_sub = list( set([item.split("_")[0] for item in os.listdir(train_val_normal)]) ) train_val_n_sub.sort(key=lambda x: x) train_n_sub, val_n_sub = ( train_val_n_sub[: int(len(train_val_n_sub) * 0.8)], train_val_n_sub[int(len(train_val_n_sub) * 0.8) :], ) print('train_n_sub:', train_n_sub) print('val_n_sub:', val_n_sub) # test abnormal subjects test_abnormal = os.path.join(root, "eval", "abnormal", channel_std) test_a_sub = list(set([item.split("_")[0] for item in os.listdir(test_abnormal)])) # test normal subjects test_normal = os.path.join(root, "eval", "normal", channel_std) test_n_sub = list(set([item.split("_")[0] for item in os.listdir(test_normal)])) # create the train, val, test sample folder if not os.path.exists(os.path.join(root, "process_refine")): os.makedirs(os.path.join(root, "process_refine")) if not os.path.exists(os.path.join(root, "process_refine", "train")): os.makedirs(os.path.join(root, "process_refine", "train")) train_dump_folder = os.path.join(root, "process_refine", "train") if not os.path.exists(os.path.join(root, "process_refine", "val")): os.makedirs(os.path.join(root, "process_refine", "val")) val_dump_folder = os.path.join(root, "process_refine", "val") if not os.path.exists(os.path.join(root, "process_refine", "test")): os.makedirs(os.path.join(root, "process_refine", "test")) test_dump_folder = os.path.join(root, "process_refine", "test") # fetch_folder, sub, dump_folder, labels parameters = [] for train_sub in train_a_sub: parameters.append([train_val_abnormal, train_sub, train_dump_folder, 1]) for train_sub in train_n_sub: parameters.append([train_val_normal, train_sub, train_dump_folder, 0]) for val_sub in val_a_sub: parameters.append([train_val_abnormal, val_sub, val_dump_folder, 1]) for val_sub in val_n_sub: parameters.append([train_val_normal, val_sub, val_dump_folder, 0]) for test_sub in test_a_sub: parameters.append([test_abnormal, test_sub, test_dump_folder, 1]) for test_sub in test_n_sub: parameters.append([test_normal, test_sub, test_dump_folder, 0]) # split and dump in parallel with Pool(processes=24) as pool: # Use the pool.map function to apply the square function to each element in the numbers list result = pool.map(split_and_dump, parameters) print('Done!')