Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery

Swati Prasad, Zheng-Hua Tan, Ramjee Prasad, Alvaro Rodrigo Cabrera, Ying Gu, Kim Dremstrup

Research output: Contribution to journalConference article in JournalResearchpeer-review

5 Citations (Scopus)

Abstract

Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we present a feature selection strategy consisting of channel selection by fisher ratio analysis in the frequency domain and time segment selection by visual inspection in time domain. The proposed strategy achieves an absolute improvement of 7.5% in the misclassification rate as compared with the baseline system that uses wavelet coefficients as features and support vector machine (SVM) as classifier
Original languageEnglish
JournalProceedings of the Wireless Personal Multimedia Communications Symposia
Pages (from-to)1-4
Number of pages4
ISSN1347-6890
Publication statusPublished - 2011
EventThe 14th International Symposium on Wireless Personal Multimedia Communications - Brest, France
Duration: 3 Oct 20116 Oct 2011

Conference

ConferenceThe 14th International Symposium on Wireless Personal Multimedia Communications
CountryFrance
CityBrest
Period03/10/201106/10/2011

Keywords

  • Brain-computer interface, feature selection, EEG classification, discrete wavelet transform, support vector machine.

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