Abstract
In this paper we used Independent component analysis model of electroencephalography (EEG) signals for preprocessing and then Hidden Markov Modeling (HMM) analysis for feature extraction from electroencephalography signal which this features are useful in Brain Computer Interface (BCI) application. Then we used Neural Networks for recognition of some diseases like epileptic seizure, Cerebral Palsy, etc.
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© 2008 Springer-Verlag Berlin Heidelberg
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Ashtiyani, M., Asadi, S., Birgani, P.M., Khordechi, E.A. (2008). EEG Classification Using Neural Networks and Independent Component Analysis. In: Abu Osman, N.A., Ibrahim, F., Wan Abas, W.A.B., Abdul Rahman, H.S., Ting, HN. (eds) 4th Kuala Lumpur International Conference on Biomedical Engineering 2008. IFMBE Proceedings, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69139-6_48
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DOI: https://doi.org/10.1007/978-3-540-69139-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69138-9
Online ISBN: 978-3-540-69139-6
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