external/CBraMod/preprocessing/preprocessing_seedvig.py

import h5py
import scipy
from scipy import signal
import os
import lmdb
import pickle
import numpy as np
import pandas as pd


data_dir = '/data/datasets/BigDownstream/SEED-VIG/mat/Raw_Data'
labels_dir = '/data/datasets/BigDownstream/SEED-VIG/mat/perclos_labels'

files = [file for file in os.listdir(data_dir)]
files = sorted(files)

files_dict = {
    'train': files[:15],
    'val': files[15:19],
    'test': files[19:23],
}

print(files_dict)

dataset = {
    'train': list(),
    'val': list(),
    'test': list(),
}

db = lmdb.open('/data/datasets/BigDownstream/SEED-VIG/processed', map_size=6000000000)

for files_key in files_dict.keys():
    for file in files_dict[files_key]:
        eeg = scipy.io.loadmat(os.path.join(data_dir, file))['EEG'][0][0][0]
        labels = scipy.io.loadmat(os.path.join(labels_dir, file))['perclos']
        print(eeg.shape, labels.shape)
        eeg = eeg.reshape(885, 8, 200, 17)
        eeg = eeg.transpose(0, 3, 1, 2)
        labels = labels[:, 0]
        print(eeg.shape, labels.shape)
        for i, (sample, label) in enumerate(zip(eeg, labels)):
            sample_key = f'{file[:-4]}-{i}'
            print(sample_key)
            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()