Created
February 29, 2016 23:11
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Opening and plotting coavriance matrices estimated from SSVEP dataset
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import numpy as np | |
import mne | |
from sklearn.cross_validation import cross_val_score, KFold | |
from pyriemann.estimation import covariances | |
import matplotlib.pyplot as plt | |
tmin, tmax = 2., 5. | |
event_id = dict(resting=1, stim13=2, stim17=3, stim21=4) | |
data_path = './' | |
fname = 'subject12/record-[2014.03.10-19.17.37]' | |
raw = mne.io.read_raw_fif(data_path + fname + '_raw.fif', | |
preload=True, add_eeg_ref=False) | |
events = mne.read_events(data_path + fname + '-eve.fif') | |
picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False) | |
raw.filter(6., 30., method='iir', picks=picks) | |
raw.plot(events=events, event_color={1:'red', 2:'blue', 3:'green', 4:'cyan' }, | |
duration=6, n_channels=8, color={'eeg':'steelblue'}, scalings={'eeg':2e-2}, | |
show_options=False, title='Raw EEG from S12') | |
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks, | |
baseline=None, preload=True, add_eeg_ref=False, verbose=False) | |
epochs.plot(title='SSVEP epochs', n_channels=8, n_epochs=4) | |
labels = epochs.events[:, -1] | |
cv = KFold(len(labels), 10, shuffle=True, random_state=42) | |
epochs_data_train = 1e3*epochs.get_data() | |
plt.figure() | |
plt.imshow(cov_data_train[0,:,:], interpolation='nearest') | |
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