Title :
EEG classification by ICA source selection of Laplacian-filtered data
Author :
Carvalhaes, Claudio G. ; Perreau-Guimaraes, Marcos ; Grosenick, Logan ; Suppes, Patrick
Author_Institution :
Center for the Study of Language & Inf., Stanford Univ., Stanford, CA, USA
fDate :
June 28 2009-July 1 2009
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.
Keywords :
electroencephalography; filtering theory; independent component analysis; medical signal processing; signal classification; EEG classification; ICA source selection; Laplacian-filtered data; auditory stimuli; binary classification experiment; brain states; electroencephalography; independent component analysis; mental imagery; spherical surface Laplacian; visual stimuli; Electrodes; Electroencephalography; Feature extraction; Filtering; Independent component analysis; Laplace equations; Linear discriminant analysis; Scalp; Spatial resolution; Spline; BCI; EEG classification; ICA; surface Laplacian;
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-3931-7
Electronic_ISBN :
1945-7928
DOI :
10.1109/ISBI.2009.5193224
