Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.jneumeth.2013.02.001 | Journal of Neuroscience Methods |
Keywords | DocType | Volume |
Dictionary learning,Orthogonal matching pursuit,Multivariate,Shift-invariance,EEG,Evoked potentials,P300 | Journal | 215 |
Issue | ISSN | Citations |
1 | 0165-0270 | 13 |
PageRank | References | Authors |
0.61 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quentin Barthelemy | 1 | 24 | 3.90 |
| Cédric Gouy-Pailler | 2 | 62 | 10.69 |
| yoann isaac | 3 | 13 | 0.95 |
| A. Souloumiac | 4 | 409 | 50.93 |
| Anthony Larue | 5 | 51 | 6.24 |
| Jérôme I. Mars | 6 | 78 | 11.17 |