File size: 2,831 Bytes
a0fd507 | 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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | import os
import mne
import numpy as np
import lmdb
import pickle
#遍历文件夹
def iter_files(rootDir):
#遍历根目录
files_H, files_MDD = [], []
for file in os.listdir(rootDir):
if 'TASK' not in file:
if 'MDD' in file:
files_MDD.append(file)
else:
files_H.append(file)
return files_H, files_MDD
selected_channels = ['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']
rootDir = '/data/datasets/MDDPHCED/files'
files_H, files_MDD = iter_files(rootDir)
files_H = sorted(files_H)
files_MDD = sorted(files_MDD)
print(files_H)
print(files_MDD)
print(len(files_H), len(files_MDD))
files_dict = {
'train':[],
'val':[],
'test':[],
}
dataset = {
'train': list(),
'val': list(),
'test': list(),
}
files_dict['train'].extend(files_H[:40])
files_dict['train'].extend(files_MDD[:42])
files_dict['val'].extend(files_H[40:48])
files_dict['val'].extend(files_MDD[42:52])
files_dict['test'].extend(files_H[48:])
files_dict['test'].extend(files_MDD[52:])
print(files_dict['train'])
print(files_dict['val'])
print(files_dict['test'])
db = lmdb.open('/data/datasets/MDDPHCED/processed_lmdb_75hz', map_size=1273741824)
for files_key in files_dict.keys():
for file in files_dict[files_key]:
raw = mne.io.read_raw_edf(os.path.join(rootDir, file), preload=True)
print(raw.info['ch_names'])
raw.pick_channels(selected_channels, ordered=True)
print(raw.info['ch_names'])
raw.resample(200)
raw.filter(l_freq=0.3, h_freq=75)
raw.notch_filter((50))
# raw.plot_psd(average=True)
eeg_array = raw.to_data_frame().values
# print(raw.info)
eeg_array = eeg_array[:, 1:]
points, chs = eeg_array.shape
print(eeg_array.shape)
a = points % (5 * 200)
print(a)
if a != 0:
eeg_array = eeg_array[:-a, :]
eeg_array = eeg_array.reshape(-1, 5, 200, chs)
eeg_array = eeg_array.transpose(0, 3, 1, 2)
print(eeg_array.shape)
label = 1 if 'MDD' in file else 0
for i, sample in enumerate(eeg_array):
sample_key = f'{file[:-4]}_{i}'
data_dict = {
'sample': sample, 'label': label
}
txn = db.begin(write=True)
txn.put(key=sample_key.encode(), value=pickle.dumps(data_dict))
txn.commit()
dataset[files_key].append(sample_key)
txn = db.begin(write=True)
txn.put(key='__keys__'.encode(), value=pickle.dumps(dataset))
txn.commit()
db.close() |