pyRiemann Motor imagery template

pyRiemann_MI_template.py

from pyriemann.estimation import Covariances
from pyriemann.tangentspace import TangentSpace

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

# load your data
X = ... # your EEG data, in format Ntrials x Nchannels X Nsamples
y = ... # the labels

# Make sure your data are band-pass filtered

# build your pipeline
clf = make_pipeline(Covariances(estimator='lwf'),
                    TangentSpace(metric='riemann'),
                    LogisticRegression())
# cross validation
auc = cross_val_score(clf, X, y, scoring='roc_auc')

print(auc.mean())