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!')