Multichannel classification of single EEG trials with independent component analysis

Authors:
Dik Kin Wong;Marcos Perreau Guimaraes;E. Timothy Uy;Logan Grosenick;Patrick Suppes
Affiliations:
Center for Study of Language and Information, Stanford University, CA;Center for Study of Language and Information, Stanford University, CA;Center for Study of Language and Information, Stanford University, CA;Center for Study of Language and Information, Stanford University, CA;Center for Study of Language and Information, Stanford University, CA
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 3
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Abstract

We have previously shown that classification of single-trial electroencephalographic (EEG) recordings is improved by the use of either a multichannel classifier or the best independent component over a single channel classifier. In this paper, we introduce a classifier that makes explicit use of multiple independent components. Two models are compared. The first (“direct”) model uses independent components as time-series inputs, while the second (“indirect”) model remixes the components back to the signal space. The direct model resulted in significantly improved classification rates when applied to two experiments using both monopolar and bipolar settings.