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. © 2009 IEEE.
CITATION STYLE
Carvalhaes, C. G., Perreau-Guimaraes, M., Grosenick, L., & Suppes, P. (2009). EEG classification by ICA source selection of laplacian-filtered data. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (pp. 1003–1006). https://doi.org/10.1109/ISBI.2009.5193224
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