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27f9443 | 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 | import pyxdf
from scipy import signal
import numpy as np
'''
Example of how to preprocess an EEG recording
Notch filter: 50., 60., 100.Hz
High Pass: 45Hz
Low Pass: 0.5Hz
Resample at: 200Hz
'''
def preprocessing_eeg(data_path):
notch = [50., 60., 100.]
target_fs = 200
highpass = 0.5
lowpass = 45
clip = 500
streams, header = pyxdf.load_xdf(data_path, verbose=False,
synchronize_clocks=True, dejitter_timestamps=True,
select_streams=[{'name': 'Quick-32r_R2_EEG'}])
# Get sample rate
fs = float(streams[0]['info']['nominal_srate'][0])
channel_information = streams[0]["info"]["desc"][0]["channels"][0]["channel"]
ch_names = [x["label"][0] for x in channel_information][:29]
# Get EEG
x = streams[0]["time_series"][:, :29].T.astype(np.float64) # (channels, time) comes as float32
# Filter and clip
for f_notch in notch:
if fs / 2 > f_notch:
[b_notch, a_notch] = signal.iirnotch(w0=f_notch, Q=f_notch / 2, fs=fs)
x = signal.filtfilt(b_notch, a_notch, x, axis=-1)
lowpass_applied = min(lowpass, fs / 2) - 0.5
[b, a] = signal.butter(N=3, Wn=[highpass, lowpass_applied], btype='bandpass', fs=fs)
x = signal.filtfilt(b, a, x, axis=-1)
x = x.clip(min=-clip, max=clip)
# Resampling
if target_fs != fs:
x = signal.resample(x, num=int(x.shape[-1] / fs * target_fs), axis=-1)
# Convert to float16 only after filtering
x = x.astype('float16')
x = x.reshape(1, x.shape[0], x.shape[1])
ch_names = np.array([c.lower().encode() for c in ch_names])
return x, ch_names
'''
Function to create patches for NeuroRVQ
'''
def create_patches(eeg_signal, maximum_patches, patch_size, channels_use):
n, c, t = eeg_signal.shape # Batch / trials, channels, time
n_time = (maximum_patches // len(channels_use))
eeg_signal = eeg_signal[:, :, :n_time * patch_size]
eeg_signal_patches = eeg_signal[:, channels_use, :]
return eeg_signal_patches, n_time |