File size: 8,490 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | import mne
import numpy as np
import os
import pickle
from tqdm import tqdm
"""
https://github.com/Abhishaike/EEG_Event_Classification
"""
def BuildEvents(signals, times, EventData):
[numEvents, z] = EventData.shape # numEvents is equal to # of rows of the .rec file
fs = 200.0
[numChan, numPoints] = signals.shape
# for i in range(numChan): # standardize each channel
# if np.std(signals[i, :]) > 0:
# signals[i, :] = (signals[i, :] - np.mean(signals[i, :])) / np.std(signals[i, :])
features = np.zeros([numEvents, numChan, int(fs) * 5])
offending_channel = np.zeros([numEvents, 1]) # channel that had the detected thing
labels = np.zeros([numEvents, 1])
offset = signals.shape[1]
signals = np.concatenate([signals, signals, signals], axis=1)
for i in range(numEvents): # for each event
chan = int(EventData[i, 0]) # chan is channel
start = np.where((times) >= EventData[i, 1])[0][0]
end = np.where((times) >= EventData[i, 2])[0][0]
# print (offset + start - 2 * int(fs), offset + end + 2 * int(fs), signals.shape)
features[i, :] = signals[
:, offset + start - 2 * int(fs) : offset + end + 2 * int(fs)
]
offending_channel[i, :] = int(chan)
labels[i, :] = int(EventData[i, 3])
return [features, offending_channel, labels]
def convert_signals(signals, Rawdata):
signal_names = {
k: v
for (k, v) in zip(
Rawdata.info["ch_names"], list(range(len(Rawdata.info["ch_names"])))
)
}
new_signals = np.vstack(
(
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
return new_signals
def readEDF(fileName):
Rawdata = mne.io.read_raw_edf(fileName, preload=True)
Rawdata.resample(200)
Rawdata.filter(l_freq=0.3, h_freq=75)
Rawdata.notch_filter((60))
_, times = Rawdata[:]
signals = Rawdata.get_data(units='uV')
RecFile = fileName[0:-3] + "rec"
eventData = np.genfromtxt(RecFile, delimiter=",")
Rawdata.close()
return [signals, times, eventData, Rawdata]
def load_up_objects(BaseDir, Features, OffendingChannels, Labels, OutDir):
for dirName, subdirList, fileList in tqdm(os.walk(BaseDir)):
print("Found directory: %s" % dirName)
for fname in fileList:
if fname[-4:] == ".edf":
print("\t%s" % fname)
try:
[signals, times, event, Rawdata] = readEDF(
dirName + "/" + fname
) # event is the .rec file in the form of an array
signals = convert_signals(signals, Rawdata)
except (ValueError, KeyError):
print("something funky happened in " + dirName + "/" + fname)
continue
signals, offending_channels, labels = BuildEvents(signals, times, event)
for idx, (signal, offending_channel, label) in enumerate(
zip(signals, offending_channels, labels)
):
sample = {
"signal": signal,
"offending_channel": offending_channel,
"label": label,
}
save_pickle(
sample,
os.path.join(
OutDir, fname.split(".")[0] + "-" + str(idx) + ".pkl"
),
)
return Features, Labels, OffendingChannels
def save_pickle(object, filename):
with open(filename, "wb") as f:
pickle.dump(object, f)
"""
TUEV dataset is downloaded from https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml
"""
root = "/data/zcb/data/TUEV/edf"
target = "/data/datasets/BigDownstream/TUEV_refine"
train_out_dir = os.path.join(target, "processed_train")
eval_out_dir = os.path.join(target, "processed_eval")
if not os.path.exists(train_out_dir):
os.makedirs(train_out_dir)
if not os.path.exists(eval_out_dir):
os.makedirs(eval_out_dir)
BaseDirTrain = os.path.join(root, "train")
fs = 200
TrainFeatures = np.empty(
(0, 16, fs)
) # 0 for lack of intialization, 22 for channels, fs for num of points
TrainLabels = np.empty([0, 1])
TrainOffendingChannel = np.empty([0, 1])
load_up_objects(
BaseDirTrain, TrainFeatures, TrainLabels, TrainOffendingChannel, train_out_dir
)
BaseDirEval = os.path.join(root, "eval")
fs = 200
EvalFeatures = np.empty(
(0, 16, fs)
) # 0 for lack of intialization, 22 for channels, fs for num of points
EvalLabels = np.empty([0, 1])
EvalOffendingChannel = np.empty([0, 1])
load_up_objects(
BaseDirEval, EvalFeatures, EvalLabels, EvalOffendingChannel, eval_out_dir
)
#transfer to train, eval, and test
root = "/data/datasets/BigDownstream/TUEV_refine"
# seed = 4523
# np.random.seed(seed)
train_files = os.listdir(os.path.join(root, "processed_train"))
train_val_sub = list(set([f.split("_")[0] for f in train_files]))
print("train val sub:", train_val_sub)
test_files = os.listdir(os.path.join(root, "processed_eval"))
train_val_sub.sort(key=lambda x: x)
train_sub = train_val_sub[: int(len(train_val_sub) * 0.8)]
val_sub = train_val_sub[int(len(train_val_sub) * 0.8) :]
print("train sub:", train_sub)
print("val sub:", val_sub)
val_files = [f for f in train_files if f.split("_")[0] in val_sub]
train_files = [f for f in train_files if f.split("_")[0] in train_sub]
if not os.path.exists(os.path.join(root, 'processed', 'processed_train')):
os.makedirs(os.path.join(root, 'processed', 'processed_train'))
if not os.path.exists(os.path.join(root, 'processed', 'processed_eval')):
os.makedirs(os.path.join(root, 'processed', 'processed_eval'))
if not os.path.exists(os.path.join(root, 'processed', 'processed_test')):
os.makedirs(os.path.join(root, 'processed', 'processed_test'))
for file in tqdm(train_files):
os.system(f"cp {os.path.join(root, 'processed_train', file)} {os.path.join(root, 'processed', 'processed_train')}")
for file in tqdm(val_files):
os.system(f"cp {os.path.join(root, 'processed_train', file)} {os.path.join(root, 'processed', 'processed_eval')}")
for file in tqdm(test_files):
os.system(f"cp {os.path.join(root, 'processed_eval', file)} {os.path.join(root, 'processed', 'processed_test')}")
print('Done!')
|