Abstract
Investigation of the states of human brain through the elec-troencephalograph (EEG) is an important application of EEG signals. This paper describes the application of an artificial neural network technique together with a feature extraction technique, the wavelet packet transformation, in classification of EEG signals. Feature vector is extracted by wavelet packet transform. Artificial neural network is used to recognize the brain statues. After training, the BP and RBF neural network are able to correctly classify the brain states, respectively. This method is potentially powerful for brain states classification.
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Li, Z., Shen, M., Beadle, P. (2004). Classification of EEG Signals Under Different Brain Functional States Using RBF Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_56
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DOI: https://doi.org/10.1007/978-3-540-28648-6_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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