Multivariate Temporal Dictionary Learning for EEG - Département Métrologie Instrumentation & Information Accéder directement au contenu
Article Dans Une Revue Journal of Neuroscience Methods Année : 2013

Multivariate Temporal Dictionary Learning for EEG

Résumé

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.
Fichier principal
Vignette du fichier
2013_Barthelemy_JNM.pdf (399.64 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00822997 , version 1 (15-05-2013)
hal-00822997 , version 2 (19-10-2013)

Identifiants

Citer

Quentin Barthélemy, Cedric Gouy-Pailler, Antoine Souloumiac, Anthony Larue, Jerome I. Mars. Multivariate Temporal Dictionary Learning for EEG. Journal of Neuroscience Methods, 2013, 215, pp.19-28. ⟨10.1016/j.jneumeth.2013.02.001⟩. ⟨hal-00822997v2⟩
264 Consultations
493 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More