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
Originalsprog | Engelsk |
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Tidsskrift | Proceedings of the Wireless Personal Multimedia Communications Symposia |
Sider (fra-til) | 1-4 |
Antal sider | 4 |
ISSN | 1347-6890 |
Status | Udgivet - 2011 |
Begivenhed | The 14th International Symposium on Wireless Personal Multimedia Communications - Brest, Frankrig Varighed: 3 okt. 2011 → 6 okt. 2011 |
Konference
Konference | The 14th International Symposium on Wireless Personal Multimedia Communications |
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Land/Område | Frankrig |
By | Brest |
Periode | 03/10/2011 → 06/10/2011 |