| import scipy |
| from scipy import signal |
| import os |
| import lmdb |
| import pickle |
| import mne |
|
|
| root_dir = '/data/datasets/BigDownstream/mental-arithmetic/edf' |
| files = [file for file in os.listdir(root_dir)] |
| files = sorted(files) |
| print(files) |
|
|
| files_dict = { |
| 'train':files[:56], |
| 'val':files[56:64], |
| 'test':files[64:], |
| } |
| print(files_dict) |
| dataset = { |
| 'train': list(), |
| 'val': list(), |
| 'test': list(), |
| } |
|
|
|
|
| selected_channels = ['EEG Fp1', 'EEG Fp2', 'EEG F3', 'EEG F4', 'EEG F7', 'EEG F8', 'EEG T3', 'EEG T4', |
| 'EEG C3', 'EEG C4', 'EEG T5', 'EEG T6', 'EEG P3', 'EEG P4', 'EEG O1', 'EEG O2', |
| 'EEG Fz', 'EEG Cz', 'EEG Pz', 'EEG A2-A1'] |
|
|
|
|
|
|
| db = lmdb.open('/data/datasets/BigDownstream/mental-arithmetic/processed', map_size=1000000000) |
| for files_key in files_dict.keys(): |
| for file in files_dict[files_key]: |
| raw = mne.io.read_raw_edf(os.path.join(root_dir, file), preload=True) |
| raw.pick(selected_channels) |
| raw.reorder_channels(selected_channels) |
| raw.resample(200) |
|
|
| eeg = raw.get_data(units='uV') |
| chs, points = eeg.shape |
| a = points % (5 * 200) |
| if a != 0: |
| eeg = eeg[:, :-a] |
| eeg = eeg.reshape(20, -1, 5, 200).transpose(1, 0, 2, 3) |
| label = int(file[-5]) |
|
|
| for i, sample in enumerate(eeg): |
| sample_key = f'{file[:-4]}-{i}' |
| |
| data_dict = { |
| 'sample':sample, 'label':label-1 |
| } |
| 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() |
