import mne
import numpy as np
raw = mne.io.read_raw_fif('data_raw.fif', preload=True)
raw.filter(l_freq=0.1, h_freq=40.)
raw.notch_filter(freqs=[50, 100])
ica = mne.preprocessing.ICA(n_components=20, random_state=97, max_iter=800)
ica.fit(raw.copy().filter(l_freq=1., h_freq=None))
eog_idx, _ = ica.find_bads_eog(raw)
ica.exclude = eog_idx
ica.apply(raw)
raw.set_eeg_reference('average')
events, event_id = mne.events_from_annotations(raw)
epochs = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5,
baseline=(None, 0), preload=True,
reject=dict(eeg=150e-6))
evoked = epochs['target'].average()
evoked.plot_joint()
freqs = np.arange(4, 30, 2)
power = epochs.compute_tfr(method="morlet", freqs=freqs, n_cycles=freqs / 2.)
power.plot()
noise_cov = mne.compute_covariance(epochs, tmax=0., method='auto')
fwd = mne.read_forward_solution('sample-fwd.fif')
inv = mne.minimum_norm.make_inverse_operator(epochs.info, fwd, noise_cov)
stc = mne.minimum_norm.apply_inverse(evoked, inv, lambda2=1./9., method='dSPM')
from mne.decoding import SlidingEstimator, cross_val_multiscore
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
X = epochs.get_data(copy=True)
y = epochs.events[:, -1]
clf = make_pipeline(StandardScaler(), LogisticRegression(solver='liblinear'))
slider = SlidingEstimator(clf, scoring='roc_auc')
scores = cross_val_multiscore(slider, X, y, cv=5)
from mne.stats import spatio_temporal_cluster_test
adjacency, _ = mne.channels.find_ch_adjacency(epochs.info, 'eeg')
T_obs, clusters, p_values, H0 = spatio_temporal_cluster_test(
[X_cond1, X_cond2], adjacency=adjacency, n_permutations=1000)
