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%matplotlib inline
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from sklearn.cross_validation import StratifiedKFold
import mne
from mne.datasets import sample
from mne.decoding import TimeDecoding, GeneralizationAcrossTime
data_path = sample.data_path()
plt.close('all')
Set parameters
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raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
tmin, tmax = -0.2, 0.5
event_id = dict(aud_l=1, vis_l=3)
# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname, preload=True, add_eeg_ref=False)
raw.set_eeg_reference() # set EEG average reference
raw.filter(2, None, method='iir') # replace baselining with high-pass
events = mne.read_events(event_fname)
# Set up pick list: EEG + MEG - bad channels (modify to your needs)
raw.info['bads'] += ['MEG 2443', 'EEG 053'] # bads + 2 more
picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=True, eog=True,
exclude='bads')
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, baseline=None, preload=True,
reject=dict(grad=4000e-13, eog=150e-6), add_eeg_ref=False)
epochs_list = [epochs[k] for k in event_id]
mne.epochs.equalize_epoch_counts(epochs_list)
data_picks = mne.pick_types(epochs.info, meg=True, exclude='bads')
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td = TimeDecoding(predict_mode='cross-validation', n_jobs=1)
# Fit
td.fit(epochs)
# Compute accuracy
td.score(epochs)
# Plot scores across time
td.plot(title='Sensor space decoding')
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# make response vector
y = np.zeros(len(epochs.events), dtype=int)
y[epochs.events[:, 2] == 3] = 1
cv = StratifiedKFold(y=y) # do a stratified cross-validation
# define the GeneralizationAcrossTime object
gat = GeneralizationAcrossTime(predict_mode='cross-validation', n_jobs=1,
cv=cv, scorer=roc_auc_score)
# fit and score
gat.fit(epochs, y=y)
gat.score(epochs)
# let's visualize now
gat.plot()
gat.plot_diagonal()
Have a look at the example
sphx_glr_auto_examples_decoding_plot_decoding_csp_space.py
.. [1] Jean-Remi King, Alexandre Gramfort, Aaron Schurger, Lionel Naccache and Stanislas Dehaene, "Two distinct dynamic modes subtend the detection of unexpected sounds", PLOS ONE, 2013, http://www.ncbi.nlm.nih.gov/pubmed/24475052
.. [2] King & Dehaene (2014) 'Characterizing the dynamics of mental representations: the temporal generalization method', Trends In Cognitive Sciences, 18(4), 203-210. http://www.ncbi.nlm.nih.gov/pubmed/24593982