Evaluation of Hidden Markov Model for p300 detection in EEG signal.
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.
Rastjoo
Ali
A
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Iran.
Arabalibeik
Hossein
H
D004569:Electroencephalography; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D006801:Humans; D008390:Markov Chains; D009415:Nerve Net