Decoding sensor space data with generalization across time and conditions

This example runs the analysis described in [1]. It illustrates how one can fit a linear classifier to identify a discriminatory topography at a given time instant and subsequently assess whether this linear model can accurately predict all of the time samples of a second set of conditions.

References

[1]King & Dehaene (2014) ‘Characterizing the dynamics of mental representations: the Temporal Generalization method’, Trends In Cognitive Sciences, 18(4), 203-210. doi: 10.1016/j.tics.2014.01.002.
../../_images/sphx_glr_plot_decoding_time_generalization_conditions_001.png

Out:

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Current compensation grade : 0
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Setting up band-pass filter from 1 - 30 Hz
l_trans_bandwidth chosen to be 1.0 Hz
h_trans_bandwidth chosen to be 7.5 Hz
Filter length of 497 samples (3.310 sec) selected
288 matching events found
No baseline correction applied
Not setting metadata
Created an SSP operator (subspace dimension = 3)
4 projection items activated
Loading data for 288 events and 69 original time points ...
    Rejecting  epoch based on MAG : ['MEG 1711']
1 bad epochs dropped

# Authors: Jean-Remi King <jeanremi.king@gmail.com>
#          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#          Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)

import matplotlib.pyplot as plt

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

import mne
from mne.datasets import sample
from mne.decoding import GeneralizingEstimator

print(__doc__)

# 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.read_raw_fif(raw_fname, preload=True)
picks = mne.pick_types(raw.info, meg=True, exclude='bads')  # Pick MEG channels
raw.filter(1., 30., fir_design='firwin')  # Band pass filtering signals
events = mne.read_events(events_fname)
event_id = {'Auditory/Left': 1, 'Auditory/Right': 2,
            'Visual/Left': 3, 'Visual/Right': 4}
tmin = -0.050
tmax = 0.400
decim = 2  # decimate to make the example faster to run
epochs = mne.Epochs(raw, events, event_id=event_id, tmin=tmin, tmax=tmax,
                    proj=True, picks=picks, baseline=None, preload=True,
                    reject=dict(mag=5e-12), decim=decim)

# We will train the classifier on all left visual vs auditory trials
# and test on all right visual vs auditory trials.
clf = make_pipeline(StandardScaler(), LogisticRegression())
time_gen = GeneralizingEstimator(clf, scoring='roc_auc', n_jobs=1)

# Fit classifiers on the epochs where the stimulus was presented to the left.
# Note that the experimental condition y indicates auditory or visual
time_gen.fit(X=epochs['Left'].get_data(),
             y=epochs['Left'].events[:, 2] > 2)

# Score on the epochs where the stimulus was presented to the right.
scores = time_gen.score(X=epochs['Right'].get_data(),
                        y=epochs['Right'].events[:, 2] > 2)

# Plot
fig, ax = plt.subplots(1)
im = ax.matshow(scores, vmin=0, vmax=1., cmap='RdBu_r', origin='lower',
                extent=epochs.times[[0, -1, 0, -1]])
ax.axhline(0., color='k')
ax.axvline(0., color='k')
ax.xaxis.set_ticks_position('bottom')
ax.set_xlabel('Testing Time (s)')
ax.set_ylabel('Training Time (s)')
ax.set_title('Generalization across time and condition')
plt.colorbar(im, ax=ax)
plt.show()

Total running time of the script: ( 0 minutes 5.805 seconds)

Gallery generated by Sphinx-Gallery