ERP EEG decoding in Tangent space.ΒΆ

Decoding applied to EEG data in sensor space decomposed using Xdawn. After spatial filtering, covariances matrices are estimated, then projected in the tangent space and classified with a logistic regression.

# Authors: Alexandre Barachant <>
# License: BSD (3-clause)

import numpy as np

from pyriemann.estimation import XdawnCovariances
from pyriemann.tangentspace import TangentSpace
from pyriemann.utils.viz import plot_confusion_matrix

import mne
from mne import io
from mne.datasets import sample

from sklearn.pipeline import make_pipeline
from sklearn.cross_validation import KFold
from sklearn.linear_model import LogisticRegression

from matplotlib import pyplot as plt


data_path = sample.data_path()

Set parameters and read data

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
event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)

# Setup for reading the raw data
raw = io.Raw(raw_fname, preload=True, verbose=False)
raw.filter(2, None, method='iir')  # replace baselining with high-pass
events = mne.read_events(event_fname)['bads'] = ['MEG 2443']  # set bad channels
picks = mne.pick_types(, meg=False, eeg=True, stim=False, eog=False,

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

labels =[:, -1]
evoked = epochs.average()

Decoding in tangent space with a logistic regression

n_components = 2  # pick some components

# Define a monte-carlo cross-validation generator (reduce variance):
cv = KFold(len(labels), 10, shuffle=True, random_state=42)
epochs_data = epochs.get_data()

clf = make_pipeline(XdawnCovariances(n_components),

preds = np.zeros(len(labels))

for train_idx, test_idx in cv:
    y_train, y_test = labels[train_idx], labels[test_idx][train_idx], y_train)
    preds[test_idx] = clf.predict(epochs_data[test_idx])

# Printing the results
acc = np.mean(preds == labels)
print("Classification accuracy: %f " % (acc))

names = ['audio left', 'audio right', 'vis left', 'vis right']
plot_confusion_matrix(preds, labels, names)

Script output:

Classification accuracy: 0.902778

Total running time of the script: (0 minutes 6.666 seconds)

Download Python source code: