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Title: Brain imaging and support vector machines for brain computer interface
Authors: Khachab, Maha 
Kaakour, Salim
Mokbel, Chafic 
Affiliations: Faculty of Medicine 
Department of Electrical Engineering 
Keywords: Support Vector Machine (SVM)
Feature extraction
Handicapped aids
Image classification
Medical image processing
Subjects: Electroencephalography
Issue Date: 2007
Publisher: IEEE
Part of: 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007.
Start page: 1032
End page: 1035
Conference: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (4th : 12-15 April 2007 : Arlington, VA, USA) 
Signal subspace correlation methods are used to derive EEG features for a brain computer interface (BCI) system. The "multiple signal classification" (MUSIC) algorithm was applied to scan a single dipole model through a grid confined to a three dimensional head model. The projection onto an estimated signal subspace was then computed to extract relevant features that were provided to a classifier whose aim was to determine the request conveyed by the user. Two classifiers, the multilayer perceptron (MLP) and the support vector machines (SVM) were tested and compared. The use of SVM with features extracted from signal subspace correlation yielded an error rate of 17% on a reference database suggesting that the proposed BCI system shows better results than the known state of the art systems.
Open URL: Link to full text
Type: Conference Paper
Appears in Collections:Faculty of Medicine

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