Date(s) : 14/03/2013 iCal
14 h 00 min - 15 h 00 min
Dictionary learning and application to EEG\n\nBy Quentin Barthelemy\, CEA. \n\nThis presentation 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 talk 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.