EVALUATION OF THE HIDDEN MARKOV MODEL FOR DETECTION OF P300 IN EEG SIGNALS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

RASTJOU ARDAKANI A.; ARAB ALI BEYK H.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF MEDICAL PHYSICS Year:2009 | Volume:5 | Issue:2 (20-21) Page(s): 25-38

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,902

Download:

Cites:

Information Journal Paper

Title

EVALUATION OF THE HIDDEN MARKOV MODEL FOR DETECTION OF P300 IN EEG SIGNALS

Author(s)

RASTJOU ARDAKANI A. | ARAB ALI BEYK H. | Issue Writer Certificate

Keywords

P300Q3

EEGQ3

BCIQ3

HMMQ2

MLPQ3

WAVELET-ENHANCED ICAQ3

WAVELET TRASFORMQ3

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.

Cites

No record.

References

No record.

Cite

APA: Copy

RASTJOU ARDAKANI, A., & ARAB ALI BEYK, H.. (2009). EVALUATION OF THE HIDDEN MARKOV MODEL FOR DETECTION OF P300 IN EEG SIGNALS. IRANIAN JOURNAL OF MEDICAL PHYSICS, 5(2 (20-21)), 25-38. SID. https://sid.ir/paper/96990/en

Vancouver: Copy

RASTJOU ARDAKANI A., ARAB ALI BEYK H.. EVALUATION OF THE HIDDEN MARKOV MODEL FOR DETECTION OF P300 IN EEG SIGNALS. IRANIAN JOURNAL OF MEDICAL PHYSICS[Internet]. 2009;5(2 (20-21)):25-38. Available from: https://sid.ir/paper/96990/en

IEEE: Copy

A. RASTJOU ARDAKANI, and H. ARAB ALI BEYK, “EVALUATION OF THE HIDDEN MARKOV MODEL FOR DETECTION OF P300 IN EEG SIGNALS,” IRANIAN JOURNAL OF MEDICAL PHYSICS, vol. 5, no. 2 (20-21), pp. 25–38, 2009, [Online]. Available: https://sid.ir/paper/96990/en

Related Journal Papers