Abstract
The correct recognition of Motor Imagery task in Brain-Computer Interface (BCI) systems has been an important issue in recent studies. In this study, we propose a classification framework based on ensemble methods to handle spectral and spatial EEG signal characteristics. A mixture of two ensemble classifiers has been used for combining multiple information sources. The performance of the proposed classifier has been evaluated on a two-class problem (right and left hand) from the BCI Competition IV dataset 2a. The used features for the training data are the selected features by Mutual information-based Best Individual Feature from the output of the Filter Bank Common Spatial Pattern. The results show that proposed method can reach an accuracy of 90.27% with just 7 features, while other methods have lower accuracy and a higher number of features.
Originalsprog | Engelsk |
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Titel | 23rd Iranian Conference on Biomedical Engineering and 1st International Iranian Conference on Biomedical Engineering, ICBME 2016 |
Antal sider | 5 |
Forlag | IEEE |
Publikationsdato | 30 mar. 2017 |
Sider | 336-340 |
Artikelnummer | 7890983 |
ISBN (Elektronisk) | 9781509034529 |
DOI | |
Status | Udgivet - 30 mar. 2017 |
Udgivet eksternt | Ja |
Begivenhed | 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering, ICBME 2016 - Tehran, Iran Varighed: 23 nov. 2016 → 25 nov. 2016 |
Konference
Konference | 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering, ICBME 2016 |
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Land/Område | Iran |
By | Tehran |
Periode | 23/11/2016 → 25/11/2016 |