Document Type: Original Paper
MSc in Medical Engineering, Medical Physics and Medical Engineering Dept., Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Assistant Professor, Medical Physics and Medical Engineering Dept., Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Introduction: Evoked potentials arisen by stimulating the brain can be utilized as a communication tool
between humans and machines. Most brain-computer interface (BCI) systems use the P300 component,
which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for
detection of P300.
Materials and Methods: The wavelet transforms, wavelet-enhanced independent component analysis (W-
ICA), and HMM combined with a multi-layer perceptron (MLP) neural network were used for P300
detection in electroencephalogram (EEG) signals. The BCI2005 competition dataset was used for their
evaluation. First, electrooculogram (EOG) artifacts in the EEG signals were removed using W-ICA. Then,
background EEG noise was suppressed using a B-Spline wavelet transform. Finally, these signals were
classified using the HMM.
Results: We used accuracy, sensitivity, specificity, positive predictive value, and negative predictive value to
evaluate the performance of the proposed algorithm. The primary results in this research show that the HMM
can perform much better using an auxiliary classifier. To this end, an MLP neural network was used to select
the classes based on the outputs of the HMM models. The classification rates obtained for 15 and 5 times
averaged test signals were 81.6% and 50.7% respectively.
Discussion and Conclusion: Based on the obtained results, we may conclude that the HMM can be used for
online P300 detection.