Evaluation of the Hidden Markov Model for Detection of P300 in EEG Signals

Document Type: Original Paper

Authors

1 MSc in Medical Engineering, Medical Physics and Medical Engineering Dept., Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran

2 Assistant Professor, Medical Physics and Medical Engineering Dept., Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Abstract

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. 

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