Multivariate Temporal Dictionary Learning for EEG

Quentin Barthélemy (1) , Cedric Gouy-Pailler (2) , Yoann Isaac (2) , Antoine Souloumiac (2) , Anthony Larue (2) , Jerome I. Mars (1)

1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
2 LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies

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.

Mots clés: Dictionary learning; orthogonal matching pursuit; multivariate; shift-invariance; EEG; evoked potentials; P300

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Quentin Barthélemy, Cedric Gouy-Pailler, Yoann Isaac, Antoine Souloumiac, Anthony Larue, et al.. Multivariate Temporal Dictionary Learning for EEG. Journal of Neuroscience Methods, 2013, 215 (1), pp.19-28. ⟨hal-00822997v1⟩