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%matplotlib inline

Decoding sensor space data

Decoding, a.k.a MVPA or supervised machine learning applied to MEG data in sensor space. Here the classifier is applied to every time point.



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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)
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))

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')

Temporal decoding

We'll use the default classifer for a binary classification problem which is a linear Support Vector Machine (SVM).



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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')

Generalization Across Time

Here we'll use a stratified cross-validation scheme.



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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()

Exercise

Can you improve the performance using full epochs and a common spatial pattern (CSP) used by most BCI systems?
Explore other datasets from MNE (e.g. Face dataset from SPM to predict Face vs. Scrambled)

Have a look at the example :ref:sphx_glr_auto_examples_decoding_plot_decoding_csp_space.py