Classification of motor imagery based on hybrid features of bispectrum of EEG

Author

Bordoloi, Sandip ; Sharmah, U. ; Hazarika, S.M.

Author_Institution

Dept. of Comput. Sci. & Eng., Tezpur Univ., Tezpur, India

fYear

2012

fDate

28-29 Dec. 2012

Firstpage

113

Lastpage

116

Abstract

Of late, several studies have established the applicability of bispectrum technique for EEG signal analysis. This paper explores hybrid features of bispectrum for classification of motor imagery (MI). Four different MI is classified based two hybrid features of bispectrum through a RBF kernel support vector machine.

Keywords

electroencephalography; image classification; medical image processing; support vector machines; EEG bispectrum; EEG signal analysis; RBF kernel support vector machine; motor imagery classification; Digital filters; Electroencephalography; Filtration; Standards; Bispectrum; Brain-computer Interfacing; Electroencephalogram; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Devices and Intelligent Systems (CODIS), 2012 International Conference on

Conference_Location

Kolkata

Print_ISBN

978-1-4673-4699-3

Type

conf

DOI

10.1109/CODIS.2012.6422149

Filename

6422149

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3105513