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EEG_preprocess.py
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import numpy as np
import mne
from mne.preprocessing import ICA
from mne.time_frequency import tfr_morlet
import scipy.io as scio
import pickle
#input_shape=(channels, samples)
def eeg_preprocess(data_model='3', data_path=None):
if data_path==None:
print('请输入数据的路径')
#博睿康59导
if data_model == 'YS':
ch_names = ['Fpz','Fp1','Fp2','AF3','AF4','AF7','AF8','Fz','F1','F2','F3','F4','F5','F6','F7','F8','FCz','FC1','FC2','FC3','FC4','FC5','FC6','FT7','FT8','Cz','C1','C2','C3','C4','C5','C6','T7','T8','CP1','CP2','CP3','CP4','CP5','CP6','TP7','TP8','Pz','P3','P4','P5','P6','P7','P8','POz',
'PO3','PO4','PO5','PO6','PO7','PO8','Oz','O1','O2']
ch_types = ['eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg'
]
sfreq = 1000
info = mne.create_info(
ch_names=ch_names,
ch_types=ch_types,
sfreq=sfreq)
info.set_montage('standard_1020')
trigger_list = [250, 251, 240, 241, 243, 243, 201, 202, 203]
dp = open(data_path, 'rb')
data = pickle.load(dp)
trigger_id_list = []
trigger_idx_list = []
for trigger_idx, trigger_id in enumerate(data[-1]):
# if trigger_id != 0:
# print(trigger_id)
if trigger_id in trigger_list:
trigger_idx_list.append(trigger_idx)
trigger_id_list.append(trigger_id)
print(len(trigger_id_list), len(trigger_idx_list))
events = np.zeros((len(trigger_id_list), 3), int)
events[:, 0] = np.squeeze(trigger_idx_list)
events[:, 2] = np.squeeze(trigger_id_list)
#课题室数据集
# structured_eeg = scio.loadmat(data_path)
# eeg_data = structured_eeg['EEG']['data'].item()
# events_list = structured_eeg["EEG"]["events"]
# events = np.zeros((len(events_list), 3), int)
# for i in range(len(events_list)):
# events[i, 0] = events_list[i, 0][1]
# events[i, 2] = events_list[i, 0][0]
raw = mne.io.RawArray(data=data[:59], info=info)
raw.plot(n_channels=59,
scalings=50,
title='YSU EEG DATA',
show=True,
block=True,
)
raw.compute_psd(fmax=60).plot() #平均功率谱密度(fmax:频率)
raw.plot_sensors(ch_type='eeg', show_names=True) #源电极
raw.plot_psd_topo(fmax=60) #电极psd
raw = raw.filter(l_freq=0.1, h_freq=30, method='fir') #method='iir
raw.compute_psd(fmax=60).plot()
#插值坏导
raw = raw.interpolate_bads()
ica = ICA(n_components=20, max_iter='auto')
raw_for_ica = raw.copy().filter(l_freq=1, h_freq=None)
ica.fit(raw_for_ica)
ica.plot_sources(raw_for_ica)
ica.plot_components(inst=raw)
x = input('输入拒绝的成分：')
if len(x) == 0:
reject=[]
elif len(x) == 1:
reject = [int(x)]
else:
xlist=x.split(',')
reject = [int(xlist[i]) for i in range(len(xlist))]
ica.exclude = reject
ica.plot_overlay(raw_for_ica, exclude=reject) #ica前后区别图
ica.apply(raw)
event_id = dict(lefthand=201, righthand=202, bothfeet=203)
picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False, ecg=False,
exclude='bads')
epochs = mne.Epochs(raw, events, event_id, tmin=0, tmax=4, proj=False, baseline=(None, None),
picks=picks, preload=False, detrend=None)
epochs.plot(n_epochs=3,
events=True,
event_id=True,
scalings=50,
title='YSU EEG DATA',
show=True,
block=True,
butterfly=True,)
epochs.compute_psd().plot_topomap()
# #08公开数据
if data_model =='2':
ch_names = ['Fpz','Fp1','Fp2','AF3','AF4','AF7','AF8','Fz','F1','F2','F3','F4','F5','F6','F7','F8','FCz','FC1','FC2','FC3','FC4','FC5','FC6','FT7','FT8','Cz','C1','C2','C3','C4','C5','C6','T7','T8','CP1','CP2','CP3','CP4','CP5','CP6','TP7','TP8','Pz','P3','P4','P5','P6','P7','P8','POz',
'PO3','PO4','PO5','PO6','PO7','PO8','Oz','O1','O2']
ch_types = ['eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',
'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg'
]
sfreq = 1000
info = mne.create_info(
ch_names=ch_names,
ch_types=ch_types,
sfreq=sfreq)
info.set_montage('standard_1020')
#课题室数据集
structured_eeg = scio.loadmat(data_path)
eeg_data = structured_eeg['EEG']['data'].item()
events_list = structured_eeg["EEG"]["event"][0][0]
events = np.zeros((len(events_list), 3), int)
# print(events_list)
for i in range(len(events_list)):
events[i, 0] = events_list[i, 0][1]
events[i, 2] = events_list[i, 0][0]
# print(events)
raw = mne.io.RawArray(data=eeg_data[0:59], info=info)
raw.plot(n_channels=59,
scalings=25,
title='YSU EEG Data',
show=True,
block=True,
)
raw.plot_psd(fmax=80, average=False) #平均功率谱密度(fmax:频率)
raw.plot_sensors(ch_type='eeg', show_names=True) #源电极
raw.plot_psd_topo(fmax=200) #电极psd
raw = raw.filter(l_freq=0.1, h_freq=30, method='fir') #method='iir
raw.plot_psd(fmax=80, average=False)
# #插值坏导
raw = raw.interpolate_bads()
# ica = ICA(n_components=20, max_iter='auto')
# raw_for_ica = raw.copy().filter(l_freq=1, h_freq=None)
# ica.fit(raw_for_ica)
# ica.plot_sources(raw_for_ica)
# ica.plot_components(inst=raw)
# x = input('输入拒绝的成分：')
# if len(x) == 0:
# reject=[]
# elif len(x) == 1:
# reject = [int(x)]
# else:
# xlist=x.split(',')
# reject = [int(xlist[i]) for i in range(len(xlist))]
# ica.exclude = reject
# ica.plot_overlay(raw_for_ica, exclude=reject) #ica前后区别图
# ica.apply(raw)
event_id = dict(lefthand=1, righthand=2)
picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False, ecg=False,
exclude='bads')
epochs = mne.Epochs(raw, events, event_id, tmin=0, tmax=4, proj=False, baseline=(0, 0.8),
picks=picks, preload=True, detrend=1)
ica = ICA(n_components=20, max_iter='auto')
epochs_for_ica = epochs.copy().filter(l_freq=1, h_freq=None)
ica.fit(epochs_for_ica)
ica.plot_sources(epochs_for_ica)
ica.plot_components(inst=epochs, title='ICA components')
x = input('输入拒绝的成分：')
if len(x) == 0:
reject=[]
elif len(x) == 1:
reject = [int(x)]
else:
xlist=x.split(',')
reject = [int(xlist[i]) for i in range(len(xlist))]
ica.exclude = reject
ica.plot_overlay(raw, exclude=reject) #ica前后区别图
ica.apply(epochs)
if __name__ == '__main__':
ys_data_path = r'F:\S08.pkl'
# eeg_preprocess(data_model='YS', data_path=ys_data_path)
# bci_data_path = r'F:\EEG.mat'
bci_data_path = r"H:\参考数据\5.17脑电数据\张孜涵\EEG.mat"
eeg_preprocess(data_model='2', data_path=bci_data_path) #08BCI以A01T 05为例