Evaluation of Hidden Markov Model for p300 detection in EEG signal
Ali Rastjoo1, Hossein Arabalibeik
1Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Iran.
Studies in Health Technology and Informatics
|April 21, 2009
Abstract:
Hidden Markov Model (HMM) was evaluated for P300 detection in electroencephalogram (EEG) signal. In some applications like the brain-computer interface (BCI), where real time detection is a concern, HMM could be a useful tool. Wavelet enhanced independent component analysis (wICA) was used for electrooculogram (EOG) artifact removal and B-spline wavelet transform for background EEG noise cancellation. HMM results are enhanced by a multilayer perceptron (MLP) neural network. Accuracy of the proposed HMM classifier is 81.6% on the validation dataset.
