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eeg_utils.py
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eeg_utils.py
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import mne
import matplotlib.pyplot as plt
from scipy.fft import fft
from scipy.io import loadmat
from scipy.signal import cwt, ricker
from scipy import signal as scipy_signal
import numpy as np
import os
from os import listdir
from os.path import isfile, join, isdir
from pathlib import Path
import csv
home = str(Path.home())
dataset_path = home + '/eeg_to_fmri'
channels_01=['Fp1', 'Fp2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2', 'F7', 'F8', 'T7', 'T8', 'P7', 'P8', 'Fz', 'Cz', 'Pz', 'Oz', 'FC1', 'FC2', 'CP1', 'CP2', 'FC5', 'FC6', 'CP5', 'CP6', 'TP9', 'TP10', 'POz', 'ECG', 'AF3', 'AF4', 'FC3', 'FC4', 'CP3', 'CP4', 'PO3', 'PO4', 'F5', 'F6', 'C5', 'C6', 'P5', 'P6', 'AF7', 'AF8', 'FT7', 'FT8', 'TP7', 'TP8', 'PO7', 'PO8', 'FT9', 'FT10', 'P9', 'P10', 'PO9', 'PO10', 'O9', 'O10', 'Fpz', 'CPz']
channels_02=["C3-T7","T7-LM","LM-CP5","CP5-P7","P7-PO7","PO7-PO3","PO3-O1","O1-Oz","PO3-P3","P3-CP1","Pz-CP1","CP1-C3","Cz-C3","Fp2-Fp1","Fp1-AF3","AF4-Fp2","AF3-F3","F4-AF4","F3-F7","F8-F4","FC1-F3","F4-FC2","F7-FC5","FC6-F8","FC5-T7","T8-FC6","Cz-Fz","Fz-FC1","FC2-Fz","T8-C4","RM-T8","CP6-RM","P8-CP6","PO8-P8","PO4-PO8","O2-PO4","Oz-O2","P4-PO4","CP2-P4","CP2-Pz","C4-CP2","C4-Cz","Pz-Oz"]
channels_03=['Fp1', 'Fp2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2', 'F7', 'F8', 'T7', 'T8', 'P7', 'P8', 'Fz', 'Cz', 'Pz', 'Oz', 'FC1', 'FC2', 'CP1', 'CP2', 'FC5', 'FC6', 'CP5', 'CP6', 'TP9', 'TP10', 'POz', 'ECG', 'F1', 'F2', 'C1', 'C2', 'P1', 'P2', 'AF3', 'AF4', 'FC3', 'FC4', 'CP3', 'CP4', 'PO3', 'PO4', 'F5', 'F6', 'C5', 'C6', 'P5', 'P6', 'AF7', 'AF8', 'FT7', 'FT8', 'TP7', 'TP8', 'PO7', 'PO8', 'FT9', 'FT10', 'Fpz', 'CPz']
channels_04=['Fp1', 'Fp2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2', 'F7', 'F8', 'T7', 'T8', 'P7', 'P8', 'Fz', 'Cz', 'Pz', 'Oz', 'FC1', 'FC2', 'CP1', 'CP2', 'FC5', 'FC6', 'CP5', 'CP6', 'TP9', 'TP10', 'POz', 'F1', 'F2', 'C1', 'C2', 'P1', 'P2', 'AF3', 'AF4', 'FC3', 'FC4', 'CP3', 'CP4', 'PO3', 'PO4', 'F5', 'F6', 'C5', 'C6', 'P5', 'P6', 'AF7', 'AF8', 'FT7', 'FT8', 'TP7', 'TP8', 'PO7', 'PO8', 'FT9', 'FT10', 'Fpz', 'CPz']
channels_05=['Fp1', 'Fp2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2', 'F7', 'F8', 'T7', 'T8', 'P7', 'P8', 'Fz', 'Cz', 'Pz', 'Oz', 'FC1', 'FC2', 'CP1', 'CP2', 'FC5', 'FC6', 'CP5', 'CP6', 'TP9', 'TP10', 'POz', 'F1', 'F2', 'C1', 'C2', 'P1', 'P2', 'AF3', 'AF4', 'FC3', 'FC4', 'CP3', 'CP4', 'PO3', 'PO4', 'F5', 'F6', 'C5', 'C6', 'P5', 'P6', 'AF7', 'AF8', 'FT7', 'FT8', 'TP7', 'TP8', 'PO7', 'PO8', 'FT9', 'FT10', 'Fpz', 'CPz']
channels_10=["A1","A2","A3","A4","A5","A6","A7","A8","A9","A10","A11","A12","A13","A14","A15","A16","A17","A18","A19","A20","A21","A22","A23","A24","A25","A26","A27","A28","A29","A30","A31","A32","B1","B2","B3","B4","B5","B6","B7","B8","B9","B10","B11","B12","B13","B14","B15","B16","B17","B18","B19","B20","B21","B22","B23","B24","B25","B26","B27","B28","B29","B30","B31","B32","C1","C2","C3","C4","C5","C6","C7","C8","C9","C10","C11","C12","C13","C14","C15","C16","C17","C18","C19","C20","C21","C22","C23","C24","C25","C26","C27","C28","C29","C30","C31","C32","D1","D2","D3","D4","D5","D6","D7","D8","D9","D10","D11","D12","D13","D14","D15","D16","D17","D18","D19","D20","D21","D22","D23","D24","D25","D26","D27","D28","D29","D30","D31","D32","EXG1","EXG2","EXG7","EXG8"]
channels_11=['Fp1', 'AF3', 'F7', 'F3', 'FC1', 'FC5', 'T7', 'C3', 'CP1', 'CP5', 'P7', 'P3', 'Pz', 'PO3', 'O1', 'Oz', 'O2', 'PO4', 'P4', 'P8', 'CP6', 'CP2', 'C4', 'T8', 'FC6', 'FC2', 'F4', 'F8', 'AF4', 'Fp2', 'Fz', 'Cz', 'EXG1', 'EXG2', 'EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8', 'Status']
channels_coords_10_20= {"Fpz": (0.5,0.9),"Fp1": (0.40,0.88),"Fp2": (0.6,0.88),"AFz": (0.5,0.8),"AF3": (0.43,0.79),"AF4": (0.57,0.79),"AF7": (0.29,0.83),"AF8": (0.71,0.83),"Fz": (0.5,0.7),"F1": (0.41,0.7),"F2": (0.59,0.7),"F3": (0.32,0.71),"F4": (0.68,0.71),"F5": (0.25,0.725),"F6": (0.75,0.725),"F7": (0.19,0.74),"F8": (0.81,0.74),"F9": (0.12,0.78),"F10": (0.88,0.78),"FCz": (0.5,0.6),"FC1": (0.4,0.6),"FC2": (0.6,0.6),"FC3": (0.3,0.605),"FC4": (0.7,0.605),"FC5": (0.22,0.615),"FC6": (0.78,0.615),"FT7": (0.13,0.63),"FT8": (0.87,0.63),"FT9": (0.05,0.655),"FT10": (0.95,0.655),"Cz": (0.5,0.5),"C1": (0.4,0.5),"C2": (0.6,0.5),"C3": (0.3,0.5),"C4": (0.7,0.5),"C5": (0.2,0.5),"C6": (0.8,0.5),"T7": (0.1,0.5),"T8": (0.9,0.5),"CPz": (0.5,0.4),"CP1": (0.4,0.4),"CP2": (0.6,0.4),"CP3": (0.3,0.395),"CP4": (0.7,0.395),"CP5": (0.22,0.385),"CP6": (0.78,0.385),"TP7": (0.13,0.37),"TP8": (0.87,0.37),"TP9": (0.05,0.345),"TP10": (0.95,0.345),"Pz": (0.5,0.3),"P1": (0.41,0.3),"P2": (0.59,0.3),"P3": (0.32,0.29),"P4": (0.68,0.29),"P5": (0.25,0.275),"P6": (0.75,0.275),"P7": (0.19,0.26),"P8": (0.81,0.26),"P9": (0.12,0.22),"P10": (0.88,0.22),"POz": (0.5,0.2),"PO3": (0.43,0.21),"PO4": (0.57,0.21),"PO7": (0.29,0.17),"PO8": (0.71,0.17),"PO9": (0.25,0.1),"PO10": (0.75,0.1),"Oz": (0.5,0.1),"O1": (0.40,0.12),"O2": (0.6,0.12),"O9": (0.37,0.05),"O10": (0.63,0.05)}
#frequency samples of each EEG dataset
fs_01=250
fs_02=1000
fs_03=5000
fs_04=200
fs_05=200
fs_NEW=None
fs_10=2048
fs_11=512
fs_12=1024
media_directory="/mnt/datasets/"
dataset_03="ds002158"
dataset_04="ds002336"
dataset_05="ds002338"
dataset_NEW="NEW"
dataset_10="ds004000"
dataset_11="ds002778"
##########################################################################################################################
#
# READING UTILS
#
##########################################################################################################################
def get_eeg_instance_01(individual, path_eeg=dataset_path+'/datasets/01/EEG/', preprocessed=True):
individuals = sorted([f for f in listdir(path_eeg) if isdir(join(path_eeg, f))])
individual = individuals[individual]
if(preprocessed):
path = path_eeg + individual + '/export/'
else:
path = path_eeg + individual + '/raw/'
brainvision_files = sorted([f for f in listdir(path) if isfile(join(path, f))])
vhdr_file = brainvision_files[1]
complete_path = path + vhdr_file
return mne.io.read_raw_brainvision(complete_path, preload=True, verbose=0)
def get_eeg_instance_02(individual, task=0, run=0, total_runs=3, preprocessed=True, path_eeg=dataset_path+'/datasets/02'):
individuals = sorted([f for f in listdir(path_eeg) if isdir(join(path_eeg, f))])
path_eeg = path_eeg + '/' + individuals[individual] + '/EEG'
runs = sorted([f for f in listdir(path_eeg) if isdir(join(path_eeg, f))])
run = runs[task*total_runs+run]
if(preprocessed):
path = path_eeg + '/' + run + '/EEG_noGA.mat'
else:
path = path_eeg + '/' + run + '/EEG_noGA.mat'
eeg_file = loadmat(path)
return eeg_file['data_noGA'][:43,:]
def get_eeg_instance_03(individual, path_eeg=media_directory+dataset_03+"/", run="main_run-001", preprocessed=False):
run_types=["main_run-001", "main_run-002",
"main_run-003", "main_run-004",
"main_run-005", "main_run-006"]
assert run in run_types, dataset_03+ " contains the following recording sessions: " + str(run_types) + ", please select one."
assert not preprocessed, "Preprocessed EEG signal is not available, only EEG events"
individuals = sorted([f for f in listdir(path_eeg) if isdir(join(path_eeg, f))])[2:]
individual = individuals[individual]
if(preprocessed):
path = path_eeg + "derivatives/eegprep/" + individual + "/ses-001/eeg/" + individual + "_ses-001_task-main_eeg_preproc.set"
return mne.io.read_epochs_eeglab(path)
else:
path = path_eeg + individual + "/ses-001/eeg/"
brainvision_files = sorted([f for f in listdir(path) if isfile(join(path, f))])
vhdr_file = brainvision_files[1]
complete_path = path + vhdr_file
return mne.io.read_raw_brainvision(complete_path, preload=True, verbose=0)
def get_eeg_instance_04(individual, path_eeg=media_directory+dataset_04+"/derivatives/", task="eegfmriNF", preprocessed=True):
assert task in ["eegNF", "eegfmriNF", "fmriNF", "motorloc"]
individuals = sorted([f for f in listdir(path_eeg) if isdir(join(path_eeg, f))])
individual = individuals[individual]
path = path_eeg + individual + "/eeg_pp/"
brainvision_files = sorted([f for f in listdir(path) if isfile(join(path, f)) and task in path+f])
vhdr_file = brainvision_files[1]
complete_path = path + vhdr_file
return mne.io.read_raw_brainvision(complete_path, preload=True, verbose=0)
def get_eeg_instance_05(individual, path_eeg=media_directory+dataset_05+"/derivatives/", task="MIpost", preprocessed=True):
assert task in ["1dNF_run-01", "1dNF_run-02", "1dNF_run-03", "MIpost", "MIpre"]
individuals = sorted([f for f in listdir(path_eeg) if isdir(join(path_eeg, f))])
individual = individuals[individual]
path = path_eeg + individual + "/eeg_pp/"
brainvision_files = sorted([f for f in listdir(path) if isfile(join(path, f)) and task in path+f])
vhdr_file = brainvision_files[1]
complete_path = path + vhdr_file
return mne.io.read_raw_brainvision(complete_path, preload=True, verbose=0)
"""
Function that reads EEG data from a <NEW> dataset
This function should return an EEG raw brainvision object, please refer to: https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw
Inputs:
* int - individual, should be an integer \in [0,NUMBER_INDIVIDUALS_NEW]
* str - path_eeg, the path specification where the individuals are listed
* str - task, optional, depends on the dataset you are operating with
* bool - preprocessed, specifies if one is reading preprocessed data or not, feel free to implement it as you wish
Outputs:
* mne.io.Raw - the EEG object
"""
def get_eeg_instance_NEW(individual, path_eeg=None, task=None, preprocessed=None):
raise NotImplementedError
def get_eeg_instance_10(individual, path_eeg=media_directory+dataset_10+"/", proposer=False, preprocessed=False):
assert not preprocessed, "Preprocessed EEG signal is not available, only EEG events"
individuals = sorted([f for f in listdir(path_eeg) if isdir(join(path_eeg, f))])[:]
individual = individuals[individual]
if(preprocessed):
path = path_eeg + "derivatives/eegprep/" + individual + "/eeg/" + individual + "_task-main_eeg_preproc.set"
return mne.io.read_epochs_eeglab(path)
else:
path = path_eeg + individual + "/eeg/"
brainvision_files = sorted([f for f in listdir(path) if "vhdr" in f])
if(proposer):
vhdr_file = brainvision_files[0]
else:
vhdr_file = brainvision_files[1]
complete_path = path + vhdr_file
return mne.io.read_raw_brainvision(complete_path, preload=True, verbose=0)
def get_eeg_instance_11(individual, path_eeg=media_directory+dataset_11+"/", sess_on=True, preprocessed=False):
assert not preprocessed, "Preprocessed EEG signal is not available, only EEG events"
individuals = sorted([f for f in listdir(path_eeg) if isdir(join(path_eeg, f))])[1:]
individual = individuals[individual]
if(preprocessed):
path = path_eeg + "derivatives/eegprep/" + individual + "/eeg/" + individual + "_task-main_eeg_preproc.set"
return mne.io.read_epochs_eeglab(path)
else:
if("hc" in individual):
session="ses-hc"
elif("pd" in individual and sess_on):
session="ses-on"
else:
session="ses-off"
path = path_eeg + individual + "/" + session + "/eeg/"
complete_path = path + individual + "_" + session + "_task-rest_eeg.bdf"
return mne.io.read_raw_bdf(complete_path, preload=True, verbose=0)
def get_labels_10(individuals, path_eeg=media_directory+dataset_10+"/"):
labels=np.zeros((len(individuals), 2))#0 - hc, 1 - p
participants_info_file = open(path_eeg+"participants.tsv", "r")
participants_info = participants_info_file.readlines()[1:]
for ind in range(len(participants_info)):
if("HC" in participants_info[ind].split("\t")[1]):
labels[ind][0] = 1.0
elif("P" in participants_info[ind].split("\t")[1]):
labels[ind][1] = 1.0
participants_info_file.close()
return labels
def get_labels_11(individuals, path_eeg=media_directory+dataset_11+"/"):
labels=np.zeros((len(individuals), 2))#0 - hc, 1 - p
individuals = sorted([f for f in listdir(path_eeg) if isdir(join(path_eeg, f))])[1:len(individuals)+1]
for ind in range(len(individuals)):
if("hc" in individuals[ind]):
labels[ind][0] = 1.0
elif("pd" in individuals[ind]):
labels[ind][1] = 1.0
return labels
def get_eeg_dataset(number_individuals=16, path_eeg=dataset_path+'/datasets/01/EEG/', preprocessed=True):
individuals = []
for i in range(number_individuals):
individuals += [get_individual(i, path_eeg=path_eeg, preprocessed=True)]
return individuals
##########################################################################################################################
#
# FREQUENCY UTILS
#
##########################################################################################################################
frequency_bands = {'delta': [0.5,4], 'theta': [4,8], 'alpha': [8,13], 'beta': [13,30], 'gamma': [30, 100]}
def compute_fft(channel, fs=128, limit=False, f_limit=134):
N = int(len(channel)/2)
fft1 = fft(channel)
if(limit):
if(fft1.shape[0]>f_limit):
return fft1[range(f_limit)]
return np.append(fft1, np.zeros((f_limit-fft1.shape[0],), dtype=np.complex))
return fft1[range(int(N/2))]
def raw_eeg(eeg, channel=0, fs=250):
signal = eeg[channel][:]
if(type(signal) is tuple):
signal, _ = signal
signal = signal.reshape((signal.shape[1]))
else:
signal = signal.reshape((signal.shape[0]))
return signal
def stft(eeg, channel=0, window_size=2, fs=250, limit=False, f_limit=134, start_time=None, stop_time=None):
signal = eeg[channel][:]
if(type(signal) is tuple):
signal, _ = signal
signal = signal.reshape((signal.shape[1]))
else:
signal = signal.reshape((signal.shape[0]))
if(start_time == None):
start_time = 0
if(stop_time == None):
stop_time = len(signal)
signal = signal[start_time:stop_time]
t = []
fs_window_size = int(window_size*fs)
Z = []
seconds = 0
for time in range(start_time, stop_time, fs_window_size)[:-1]:
fft1 = compute_fft(signal[time:time+fs_window_size], fs=fs, limit=limit, f_limit=f_limit)
N = len(signal[time:time+fs_window_size])/2
f = np.linspace (0, len(fft1), int(N/2))
#average
Z += [list(abs(fft1[1:]))]
t += [seconds]
seconds += window_size
return f[1:], np.transpose(np.array(Z)), t
def dwt(eeg, channel=0, windows=30, fs=2.0, start_time=None, stop_time=None):
signal = eeg[channel][:]
if(type(signal) is tuple):
signal, _ = signal
signal = signal.reshape((signal.shape[1]))
else:
signal = signal.reshape((signal.shape[0]))
if(start_time == None):
start_time = 0
if(stop_time == None):
stop_time = len(signal)
signal = signal[start_time:stop_time]
return cwt(signal, scipy_signal.morlet2, np.arange(1, windows))
def mutate_stft_to_bands(Zxx, frequencies, timesteps):
#frequency first dimension, time is second dimension
Z_band_mutated = []
for t in range(len(timesteps)):
intensities = []
for i in range(len(frequency_bands.keys())):
intensities += [0]
bands = dict(zip(frequency_bands.keys(), intensities))
for f in range(len(frequencies)):
for band in bands.keys():
if(frequencies[f] <= frequency_bands[band][1]):
bands[band] += Zxx[f][t]
break
Z_band_mutated += [list(bands.values()).copy()]
return np.transpose(np.array(Z_band_mutated))
##########################################################################################################################
#
# VISUALIZATION UTILS
#
##########################################################################################################################
def plot_fft(eeg, channel=0, max_freq=30000, start_time=None, stop_time=None):
y, _ = eeg[channel][:]
y = y[0]
fs = eeg.info['sfreq']
if(start_time == None):
start_time = 0
if(stop_time == None):
stop_time = len(y)
fft1 = compute_fft(y[start_time:stop_time], fs=fs)
N = int(len(y[start_time:stop_time])/2)
f = np.linspace (0, fs, N//2)
plt.figure(1)
plt.plot (f[1:max_freq], abs (fft1)[1:max_freq])
plt.title ('Magnitude of each frequency')
plt.xlabel ('Frequency (Hz)')
plt.show()
def plot_stft(eeg, channel=2, window_size=2, min_freq=None, max_freq=None, colorbar=True):
f, Zxx, t = stft(eeg, channel=channel, fs=eeg.info['sfreq'], window_size=window_size)
if(min_freq == None):
min_freq = 0
if(max_freq == None):
max_freq = len(Zxx)
Zxx = Zxx[min_freq:max_freq]
f = f[min_freq:max_freq]
amplitude = np.max(Zxx)
fig, axes = plt.subplots(1,1)
im = axes.pcolormesh(t, f, abs(Zxx), vmin=0, vmax=amplitude)
if(colorbar):
fig.colorbar(im)
fig.show()
return fig, axes