Non-invasive classification of cortical activities for brain computer interface: A variable selection approach

Author

Besserve, Michel ; Martinerie, Jacques ; Garnero, Line

Author_Institution

Lab. Neurosciences Cognitive et Imagerie Cerebrale, Univ Paris, Paris

fYear

2008

fDate

14-17 May 2008

Firstpage

1063

Lastpage

1066

Abstract

We propose to carry out a classification method for electro-encepfialographic signals (EEG), using the activities of cortical sources estimated with an EEG inverse problem. To overcome the difficulties caused by the high number of sources (approximately 10000), we use a multivariate variable selection algorithm: the zero norm Support Vector Machine (L0-SVM). This technique allows to extract a small subset of sources, which are the most useful to allow for the discrimination of the mental states. The whole approach is applied to an asynchronous Brain Computer Interface (BCI) experiment from our lab. It outperforms a method based on the direct measurement of EEG electrodes´ activities.

Keywords

electroencephalography; handicapped aids; medical computing; support vector machines; EEG electrode activity; brain computer interface; cortical activity; electroencephalographic signals; selection algorithm; zero-norm support vector machine; Brain computer interfaces; Classification algorithms; Electroencephalography; Image analysis; Input variables; Inverse problems; Performance analysis; Spatial resolution; Support vector machine classification; Support vector machines; Brain Computer Interface; EEG; Inverse problem; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on

Conference_Location

Paris

Print_ISBN

978-1-4244-2002-5

Electronic_ISBN

978-1-4244-2003-2

Type

conf

DOI

10.1109/ISBI.2008.4541183

Filename

4541183