Mahdad Jafarzadehesfahani
77 linhas
2.7 KiB
Python
77 linhas
2.7 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""
|
|
Created on Wed Aug 2 18:31:25 2023
|
|
|
|
@author: mahjaf
|
|
"""
|
|
import mne
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from mne.time_frequency import tfr_morlet, tfr_multitaper
|
|
%matplotlib qt
|
|
|
|
data_path = "P:\\3013102.01\\Data\\"
|
|
participant_session = ['NL_DNDRS_0004__ses-2']
|
|
LRLR_events = {'NL_DNDRS_0004__ses-2': [7197, 7226, 7276, 7457, 7482, 7492]}
|
|
|
|
for idx, c_subj in enumerate(participant_session):
|
|
|
|
participant_number = c_subj.split('__')[0]
|
|
session_number = c_subj.split('__')[1]
|
|
|
|
# Reading EEG data
|
|
path_EEG_L = data_path + participant_number +'\\' + session_number + '\\eeg\\EEG L.edf'
|
|
path_EEG_R = data_path + participant_number +'\\' + session_number + '\\eeg\\EEG R.edf'
|
|
|
|
# Load the EDF file
|
|
raw_EEG_L = mne.io.read_raw_edf(path_EEG_L, preload=True)
|
|
raw_EEG_R = mne.io.read_raw_edf(path_EEG_R, preload=True)
|
|
|
|
fs = int(raw_EEG_L.info['sfreq'])
|
|
|
|
# Set up the events for creating epochs
|
|
num_rows = len(LRLR_events[c_subj])
|
|
events = np.zeros((num_rows, 3), dtype = 'int')
|
|
|
|
# Fill the first column with the values from the Python list
|
|
events[:, 0] = [element * fs for element in LRLR_events[c_subj]]
|
|
events[:, 1] = 0
|
|
events[:, 2] = 1
|
|
|
|
# Create epochs for seconds 5-10 (event ID 1) and seconds 15-20 (event ID 2)
|
|
epochs_EEG_L = mne.Epochs(raw_EEG_L, events, event_id=1, tmin=0, tmax=20, baseline=None)
|
|
epochs_EEG_R = mne.Epochs(raw_EEG_R, events, event_id=1, tmin=0, tmax=20, baseline=None)
|
|
|
|
epochs_EEG_L_get_data = np.transpose(np.squeeze(epochs_EEG_L.get_data()))
|
|
epochs_EEG_R_get_data = np.transpose(np.squeeze(epochs_EEG_R.get_data()))
|
|
|
|
for item in np.arange(len(LRLR_events[c_subj])):
|
|
print(item)
|
|
plt.figure()
|
|
plt.plot(epochs_EEG_L_get_data[:, item])
|
|
plt.plot(epochs_EEG_R_get_data[:, item])
|
|
|
|
|
|
epochs.plot()
|
|
|
|
epochs.plot_psd(fmin=.1, fmax=15, tmin=0, tmax=5, average=True)
|
|
|
|
freqs = np.arange(5., 30., 1.)
|
|
|
|
# You can trade time resolution or frequency resolution or both
|
|
# in order to get a reduction in variance
|
|
|
|
n_cycles = freqs / 2.
|
|
power = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles, return_itc=False)
|
|
power.plot([0], baseline=(0., 0.1), mode='mean', vmin=, vmax=3.,
|
|
title='Sim: Using Morlet wavelet')
|
|
|
|
##
|
|
n_cycles = freqs *5
|
|
time_bandwidth = 2.0 # Least possible frequency-smoothing (1 taper)
|
|
power = tfr_multitaper(epochs, freqs=freqs, n_cycles=n_cycles,
|
|
time_bandwidth=time_bandwidth, return_itc=False)
|
|
power.plot()
|
|
# Plot results. Baseline correct based on first 100 ms.
|
|
power.plot([0], baseline=(5., 5.1), mode='mean', vmin=-1., vmax=3.,
|
|
title='Sim: Least smoothing, most variance') |