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UID:5495@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20130314T140000
DTEND;TZID=Europe/Paris:20130314T150000
DTSTAMP:20241028T225809Z
URL:https://www.i2m.univ-amu.fr/evenements/q-barthelemy-cea-dictionary-lea
 rning-and-application-to-eeg/
SUMMARY: (...): Q. Barthelemy (CEA): Dictionary learning and application to
  EEG
DESCRIPTION:: Dictionary learning and application to EEG\n\nBy Quentin Bart
 helemy\, CEA.\n\nThis presentation addresses the issue of representing ele
 ctroencephalographic (EEG) signals in an efficient way. While classical ap
 proaches use a fixed Gabor dictionary to analyze EEG signals\, this talk p
 roposes 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 mu
 ltivariate model\, and shift-invariance is used for the temporal model. Mu
 ltivariate learned kernels are informative (a few atoms code plentiful ene
 rgy) 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 b
 y the representative power of the learned dictionary and its spatial flexi
 bility. Moreover\, dictionary learning can capture interpretable patterns:
  this ability is illustrated on real data\, learning a P300 evoked potenti
 al.
CATEGORIES:Séminaire,Signal et Apprentissage
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