Title :
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
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;
Conference_Titel :
Communications, Devices and Intelligent Systems (CODIS), 2012 International Conference on
Conference_Location :
Kolkata
Print_ISBN :
978-1-4673-4699-3
DOI :
10.1109/CODIS.2012.6422149
