Multivariate temporal dictionary learning for EEG

作者：Barthelemy Q; Gouy Pailler C; Isaac Y; Souloumiac A; Larue A^*; Mars J I

来源：Journal of Neuroscience Methods, 2013, 215(1): 19-28.

DOI：10.1016/j.jneumeth.2013.02.001

• Dictionary learning
• Orthogonal matching pursuit
• Multivariate
• Shift-invariance
• EEG
• Evoked potentials
• P300

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.

• 出版日期2013-4-30
• 单位中国地震局