example_score_subset_trials.py

# Authors: Jean-Remi King <[email protected]>
#
# License: BSD (3-clause)

""" This aims at showing how the MNE GAT object can be used to score different
subsets of data"""

import copy
import numpy as np
import mne
from mne.datasets import sample
from mne.decoding import GeneralizationAcrossTime

# Preprocess data
data_path = sample.data_path()
# Load and filter data, set up epochs
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
events_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
raw = mne.io.Raw(raw_fname, preload=True)
picks = mne.pick_types(raw.info, meg=True, exclude='bads')  # Pick MEG channels
raw.filter(1, 30, method='fft')  # Band pass filtering signals
events = mne.read_events(events_fname)
event_id = {'AudL': 1, 'AudR': 2, 'VisL': 3, 'VisR': 4}
decim = 2  # decimate to make the example faster to run
epochs = mne.Epochs(raw, events, event_id, -0.050, 0.400, proj=True,
                    picks=picks, baseline=None, preload=True,
                    reject=dict(mag=5e-12), decim=decim, verbose=False)

# We will train the classifier on all left visual vs auditory trials
# and test on all right visual vs auditory trials.

# In this case, because the test data is independent from the train data,
# we test the classifier of each fold and average the respective predictions.

# Define events of interest
triggers = epochs.events[:, 2]
viz_vs_auditory = np.in1d(triggers, (1, 2)).astype(int)

gat = GeneralizationAcrossTime(n_jobs=-1)

# fit on both left and right stim
viz_vs_auditory = 1.0 + (epochs.events[:, 2] > 2)
gat.fit(epochs, y=viz_vs_auditory)
gat.predict(epochs)
gat.score(y=viz_vs_auditory)
gat.plot()

# score left
right_left = epochs.events[:, 2] % 2
gat_ = copy.deepcopy(gat)
gat_.y_pred_ = gat.y_pred_[:, :, right_left == 0]
gat_.score(y=viz_vs_auditory[right_left == 0])
gat_.plot()

# score right
gat_ = copy.deepcopy(gat)
gat_.y_pred_ = gat.y_pred_[:, :, right_left == 1]
gat_.score(y=viz_vs_auditory[right_left == 1])
gat_.plot()

@chri4354 here's an example on how to get score on subsets of data.

you have to wait until this mne-tools/mne-python#1915 has been merged, and then update your mne directory:

cd ~/path/to/mne-ptyhon
git pull https://github.com/mne-tools/mne-python 

@cmoutard This example may be relevant to you?