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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!') |