EEG classification by ICA source selection of Laplacian-filtered data

Authors:
Claudio G. Carvalhaes;Marcos Perreau-Guimaraes;Logan Grosenick;Patrick Suppes
Affiliations:
Center for the Study of Language and Information, Stanford University, Stanford, CA and Instituto de Matemática e Estatística, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, R ...;Center for the Study of Language and Information, Stanford University, Stanford, CA;Center for the Study of Language and Information, Stanford University, Stanford, CA;Center for the Study of Language and Information, Stanford University, Stanford, CA
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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A spline framework for estimating the eeg surface laplacian using the euclidean metric

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Abstract

We studied the performance of a double-spatial filtering method for classification of single-trial electroencephalography (EEG) data that couples the spherical surface Laplacian (SL) and independent component analysis (ICA). This method was evaluated in the context of a binary classification experiment with brain states driven by mental imagery of auditory and visual stimuli. A statistically significant improvement was achieved with respect to the rates provided by raw data and by data filtered by either SL or ICA.