File size: 3,180 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 | import scipy
from scipy import signal
import os
import lmdb
import pickle
import numpy as np
import mne
tasks = ['04', '06', '08', '10', '12', '14'] # select the data for motor imagery
root_dir = '/data/datasets/eeg-motor-movementimagery-dataset-1.0.0/files'
files = [file for file in os.listdir(root_dir)]
files = sorted(files)
files_dict = {
'train': files[:70],
'val': files[70:89],
'test': files[89:109],
}
print(files_dict)
dataset = {
'train': list(),
'val': list(),
'test': list(),
}
selected_channels = ['Fc5.', 'Fc3.', 'Fc1.', 'Fcz.', 'Fc2.', 'Fc4.', 'Fc6.', 'C5..', 'C3..', 'C1..', 'Cz..', 'C2..',
'C4..', 'C6..', 'Cp5.', 'Cp3.', 'Cp1.', 'Cpz.', 'Cp2.', 'Cp4.', 'Cp6.', 'Fp1.', 'Fpz.', 'Fp2.',
'Af7.', 'Af3.', 'Afz.', 'Af4.', 'Af8.', 'F7..', 'F5..', 'F3..', 'F1..', 'Fz..', 'F2..', 'F4..',
'F6..', 'F8..', 'Ft7.', 'Ft8.', 'T7..', 'T8..', 'T9..', 'T10.', 'Tp7.', 'Tp8.', 'P7..', 'P5..',
'P3..', 'P1..', 'Pz..', 'P2..', 'P4..', 'P6..', 'P8..', 'Po7.', 'Po3.', 'Poz.', 'Po4.', 'Po8.',
'O1..', 'Oz..', 'O2..', 'Iz..']
db = lmdb.open('/data/datasets/eeg-motor-movementimagery-dataset-1.0.0/processed_average', map_size=4614542346)
for files_key in files_dict.keys():
for file in files_dict[files_key]:
for task in tasks:
raw = mne.io.read_raw_edf(os.path.join(root_dir, file, f'{file}R{task}.edf'), preload=True)
raw.pick_channels(selected_channels, ordered=True)
if len(raw.info['bads']) > 0:
print('interpolate_bads')
raw.interpolate_bads()
raw.set_eeg_reference(ref_channels='average')
raw.filter(l_freq=0.3, h_freq=None)
raw.notch_filter((60))
raw.resample(200)
events_from_annot, event_dict = mne.events_from_annotations(raw)
epochs = mne.Epochs(raw,
events_from_annot,
event_dict,
tmin=0,
tmax=4. - 1.0 / raw.info['sfreq'],
baseline=None,
preload=True)
data = epochs.get_data(units='uV')
events = epochs.events[:, 2]
print(data.shape, events)
data = data[:, :, -800:]
bz, ch_nums, _ = data.shape
data = data.reshape(bz, ch_nums, 4, 200)
print(data.shape)
for i, (sample, event) in enumerate(zip(data, events)):
if event != 1:
sample_key = f'{file}R{task}-{i}'
data_dict = {
'sample': sample, 'label': event - 2 if task in ['04', '08', '12'] else event
}
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()
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