Motor imagery data classification for BCI application using wavelet packet feature extraction
Contribuinte(s) |
Loo,CK Yap,KS Wong,KW Teoh,A Huang,K |
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Data(s) |
01/01/2014
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Resumo |
The noninvasive brain imaging modalities have provided us an extraordinary means for monitoring the working brain. Among these modalities, Electroencephalography (EEG) is the most widely used technique for measuring the brain signals under different tasks, due to its mobility, low cost, and high temporal resolution. In this paper we investigate the use of EEG signals in brain-computer interface (BCI) systems. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
http://dro.deakin.edu.au/eserv/DU:30071657/hettiarachchi-motorimagery-2014.pdf http://dro.deakin.edu.au/eserv/DU:30071657/hettiarachchi-motorimagery-evid-2014.pdf http://www.dx.doi.org/10.1007/978-3-319-12643-2_63 |
Direitos |
2014, Springer |
Palavras-Chave | #Brain-computer interface #Fisher distance criterion #Motor imagery data #Receiver operating characteristic curve #Wavelet packet decomposition #Science & Technology #Technology #Computer Science, Artificial Intelligence #Computer Science, Information Systems #Computer Science, Theory & Methods #Computer Science |
Tipo |
Book Chapter |