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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wong, D.K., Guimaraes, M.P., Uy, E.T., Grosenick, L., Suppes, P. (2006). Multichannel Classification of Single EEG Trials with Independent Component Analysis. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_79
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DOI: https://doi.org/10.1007/11760191_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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