Linear classifier on sensor data with plot patterns and filters¶

Here decoding, a.k.a MVPA or supervised machine learning, is applied to M/EEG data in sensor space. Fit a linear classifier with the LinearModel object providing topographical patterns which are more neurophysiologically interpretable [1] than the classifier filters (weight vectors). The patterns explain how the MEG and EEG data were generated from the discriminant neural sources which are extracted by the filters. Note patterns/filters in MEG data are more similar than EEG data because the noise is less spatially correlated in MEG than EEG.

References¶

[1]	Haufe, S., Meinecke, F., Görgen, K., Dähne, S., Haynes, J.-D., Blankertz, B., & Bießmann, F. (2014). On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage, 87, 96–110. doi:10.1016/j.neuroimage.2013.10.067

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

import mne
from mne import io, EvokedArray
from mne.datasets import sample
from mne.decoding import Vectorizer, get_coef

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

# import a linear classifier from mne.decoding
from mne.decoding import LinearModel

print(__doc__)

data_path = sample.data_path()

Set parameters

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 = -0.1, 0.4
event_id = dict(aud_l=1, vis_l=3)

# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname, preload=True)
raw.filter(.5, 25, fir_design='firwin')
events = mne.read_events(event_fname)

# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                    decim=2, baseline=None, preload=True)

labels = epochs.events[:, -1]

# get MEG and EEG data
meg_epochs = epochs.copy().pick_types(meg=True, eeg=False)
meg_data = meg_epochs.get_data().reshape(len(labels), -1)

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 0.5 - 25 Hz
l_trans_bandwidth chosen to be 0.5 Hz
h_trans_bandwidth chosen to be 6.2 Hz
Filter length of 991 samples (6.600 sec) selected
145 matching events found
No baseline correction applied
Not setting metadata
Created an SSP operator (subspace dimension = 4)
4 projection items activated
Loading data for 145 events and 76 original time points ...
0 bad epochs dropped

Decoding in sensor space using a LogisticRegression classifier

clf = LogisticRegression()
scaler = StandardScaler()

# create a linear model with LogisticRegression
model = LinearModel(clf)

# fit the classifier on MEG data
X = scaler.fit_transform(meg_data)
model.fit(X, labels)

# Extract and plot spatial filters and spatial patterns
for name, coef in (('patterns', model.patterns_), ('filters', model.filters_)):
    # We fitted the linear model onto Z-scored data. To make the filters
    # interpretable, we must reverse this normalization step
    coef = scaler.inverse_transform([coef])[0]

    # The data was vectorized to fit a single model across all time points and
    # all channels. We thus reshape it:
    coef = coef.reshape(len(meg_epochs.ch_names), -1)

    # Plot
    evoked = EvokedArray(coef, meg_epochs.info, tmin=epochs.tmin)
    evoked.plot_topomap(title='MEG %s' % name, time_unit='s')

Let’s do the same on EEG data using a scikit-learn pipeline

X = epochs.pick_types(meg=False, eeg=True)
y = epochs.events[:, 2]

# Define a unique pipeline to sequentially:
clf = make_pipeline(
    Vectorizer(),                       # 1) vectorize across time and channels
    StandardScaler(),                   # 2) normalize features across trials
    LinearModel(LogisticRegression()))  # 3) fits a logistic regression
clf.fit(X, y)

# Extract and plot patterns and filters
for name in ('patterns_', 'filters_'):
    # The `inverse_transform` parameter will call this method on any estimator
    # contained in the pipeline, in reverse order.
    coef = get_coef(clf, name, inverse_transform=True)
    evoked = EvokedArray(coef, epochs.info, tmin=epochs.tmin)
    evoked.plot_topomap(title='EEG %s' % name[:-1], time_unit='s')

Total running time of the script: ( 0 minutes 6.576 seconds)

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