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

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

4 Citationer (Scopus)

Abstrakt

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
OriginalsprogEngelsk
TidsskriftProceedings of the Wireless Personal Multimedia Communications Symposia
Sider (fra-til)1-4
Antal sider4
ISSN1347-6890
StatusUdgivet - 2011
BegivenhedThe 14th International Symposium on Wireless Personal Multimedia Communications - Brest, Frankrig
Varighed: 3 okt. 20116 okt. 2011

Konference

KonferenceThe 14th International Symposium on Wireless Personal Multimedia Communications
LandFrankrig
ByBrest
Periode03/10/201106/10/2011

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