  This article addresses the issue of representing electroencephalographic
(EEG) signals in an efficient way. While classical approaches use a fixed Gabor
dictionary to analyze EEG signals, this article proposes a data-driven method
to obtain an adapted dictionary. To reach an efficient dictionary learning,
appropriate spatial and temporal modeling is required. Inter-channels links are
taken into account in the spatial multivariate model, and shift-invariance is
used for the temporal model. Multivariate learned kernels are informative (a
few atoms code plentiful energy) and interpretable (the atoms can have a
physiological meaning). Using real EEG data, the proposed method is shown to
outperform the classical multichannel matching pursuit used with a Gabor
dictionary, as measured by the representative power of the learned dictionary
and its spatial flexibility. Moreover, dictionary learning can capture
interpretable patterns: this ability is illustrated on real data, learning a
P300 evoked potential.
