title: Multivariate temporal dictionary learning for EEG.
abstract: 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.
authors: CEA, LIST, Data Analysis Tools Laboratory, Gif-sur-Yvette Cedex 91191, France. quentin.barthelemy@cea.fr
authors: Q
authors: Q
authors: Barthélemy
authors: C
authors: C
authors: Gouy-Pailler
authors: Y
authors: Y
authors: Isaac
authors: A
authors: A
authors: Souloumiac
authors: A
authors: A
authors: Larue
authors: J I
authors: JI
authors: Mars
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods* / Q000706:statistics & numerical data; D058407:Electroencephalography Phase Synchronization; D018913:Event-Related Potentials, P300; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software