Discriminative channel selection method for the recognition of anticipation related potentials from CCD estimated cortical activity

G. Garipelli, R. Chavarriaga, F. Cincotti, F. Babiloni, J. D R Millan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recognition of brain states and subject's intention from electroencephalogram (EEG) is a challenging problem for braincomputer interaction. Signals recorded from each of EEG electrodes represent noisy spatio-temporal overlapping of activity arising from very diverse brain regions. However, un-mixing methods such as Cortical Current Density (CCD) can be used for estimating activity of different brain regions. These methods not only improve spatial resolution but also signal to noise ratio, hence the classifiers computed using this activity may ameliorate recognition performances. However, these methods lead to a multiplied number of channels, leading to the question - "How to choose relevant and discriminant channels from a large number of channels?". In the current paper we present a channel selection method and discuss its application to the recognition of anticipation related potentials from surface EEG channels and CCD estimated cortical potentials. We compare the classification accuracies with previously reported performances obtained using Cz electrode potentials of 9 subjects (3 experienced + 6 naive). As hypothesized, we observed improvements for most subjects with channel selection method applied to CCD activity as compared to surface-EEG channels and baseline performances. This improvement is particularly significant for subjects who are naive and did not show a clear pattern on ERP grand averages.

Original languageEnglish
Title of host publicationMachine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
DOIs
Publication statusPublished - 2009
EventMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 - Grenoble, France
Duration: Sep 2 2009Sep 4 2009

Other

OtherMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009
Country/TerritoryFrance
CityGrenoble
Period9/2/099/4/09

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Education

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