Evaluation of the Hidden Markov Model for Detection of P۳۰۰ in EEG Signals
محل انتشار: مجله فیزیک پزشکی ایران، دوره: 5، شماره: 2
سال انتشار: 1387
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 76
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شناسه ملی سند علمی:
JR_IJMP-5-2_003
تاریخ نمایه سازی: 5 شهریور 1402
چکیده مقاله:
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 P۳۰۰ component, which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for detection of P۳۰۰. 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 P۳۰۰ detection in electroencephalogram (EEG) signals. The BCI۲۰۰۵ 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 ۱۵ and ۵ times averaged test signals were ۸۱.۶% and ۵۰.۷% respectively. Discussion and Conclusion: Based on the obtained results, we may conclude that the HMM can be used for online P۳۰۰ detection.
کلیدواژه ها:
نویسندگان
Ali Rastjoo Ardakani
MSc in Medical Engineering, Medical Physics and Medical Engineering Dept., Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Hossein Arabalibeik
Assistant Professor, Medical Physics and Medical Engineering Dept., Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran