EEG Features Extraction for Classification of Human Intention and Non-Intention
Main Article Content
Abstract
This study is to classify brain's state Intention and Non-Intention base on only one channel of the Electroencephalogram (EEG) signal, then apply results to real world's problem. Because the brain signal is much different between different people, this article only discusses personal EEG data. First, data is recorded by EEG-SMT device of Olimex Ltd. Then we extracted features from collected EEG data and used the ANOVA tool to evaluate significant of them. Multilayer Neutral Network is used for the training process. The accuracy is normally above 90% for each subject by using proposed features. Finally, a method of smoothing real-time results is developed to improve training results and to play simple computer games.
Keywords
EEG, Intention, Non-Intention
Article Details
References
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[4] Z. Bahri, S. Abdulaal, M. Buallay, Sub-Band-Power- Based Efficient Brain Computer Interface for Wheelchair Control. American Journal of Signal Processing. Vol. 4 No. 1, (2014) 34-40.
[5] T. Pham, W. Ma, D. Tran, and P. Nguyen, Multi- factor EEG-based user authentication. Proceedings of International Joint Conference on Neural Networks (IJCNN), (2014) 4029-4034.
[6] Y. Liu, O. Sourina and M. K. Nguyen, Real-Time EEG-Based Human Emotion Recognition and Visualization. Proceeding of International Conference on Cyberworlds, (2010) 262-269.
[7] https://www.olimex.com/Products/EEG/OpenEEG/E EG-SMT/open-source-hardware
[8] J. F. Echallier, F. Perrin and J. Pernier, Computer- assisted placement of electrodes on the human head, Electroencephalography and clinical Neurophysiology, 82 (1992) 160-163.
[9] J. W. Bang, J. S. Choi, K. R. Park, Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images. Sensors, 13 (2013) 6272-6294.
[10] R. S. Huang, L. L. Tsai, C. J. Kuo, Selection of Valid and Reliable EEG Features for Predicting Auditory and Visual Alertness Levels. Proc. Natl. Sci. Counc. ROC(B), Vol. 25, No. 1, (2001) 17-25.
[11] N. H. Liu, C. Y. Chiang, and H. C. Chu, Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors 13.8 (2013) 10273-10286.
[12] C. Hasegawa, K. Oguri, The effects of Specific Musical Stimuli on Driver's Drowsiness, Proceeding of the Intelligent Transportation Systems Conference, ITSC'06, Tornto, ON, Canada, (2006) 817-822.
[13] M. Nandish, M. Stafford, K. P. Hemanth, F. Ahmed, Feature Extraction and Classification of EEG Signal Using Neural Network Based Techniques, International Journal of Engineering and Innovative Technology (IJEIT) (2008), volume 2, issue 4.
[14] H. Jianfeng, D. Xiao, and Z. Mu, Application of Energy Entropy in Motor Imagery EEG Classification, JDCTA 3.2 (2009) 83-90.
[15] Q. J. Zhang, K. C. Gupta, and V. K. Devabhaktuni, Artificial neural networks for RF and microwave design-from theory to practice, Microwave Theory and Techniques, IEEE Transactions on 51.4 (2003): 1339-1350.
[16] M. N. S. Swamy, Ke-Lin Du, Neural Networks and Statistical Learning, (2013) pp. 21-22.