Classification of Three Class Hand Imagery Movement with the Application of 2-Stage SVM Model
Main Article Content
Abstract
Classification of multiple motor states of human brain waves will improve the applicable ability to brain computer interfacing system and neuroprosthesis. In this research, we aim at extending classifier ability to three class imagery movement states including: left hand imagery movement, right hand imagery movement and rest state. We propose to implement new classifier using combination of discriminated features for input feature vector and classifying model based on 2-stage SVM. For the feature vector construction, ANOVA- based feature selection method is proposed to use which resulting in 14% reduction of number of features required for the process. The avarage classification accuracy of our proposed system is achived with 80,75% when evaluated on Physionet dataset. The study demonstrate that proposed IHMv classifier can distinguish more output classes than other researches with less number of electrodes while maintaining similar accuracy classification. Therefore, our system can be applied to BCI system to create controlling commands to peripheral devices or computer application through motor brain waves.
Keywords
EEG, 3-class imagery hand movement, 2-stage SVM model, Wavelet, motor cortex, ANOVA
Article Details
References
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[13] Mohammad H. Alomari, A. S. (2013). Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning. International Journal of Advanced Computer Science and Applications,, 4(6), 207-212.
[14] S.S. Keerthi, C. L. (2003). Asymptotic behavior of support vector machines with Gaussian Kernel. Neural Comput, 15(7), 1667-1689.
[2] C.Vigneshwari, V. S. (2013). Analysis of Finger Movements Using EEG Signal. International Journal of Emerging Technology and Advanced Engineering. 3(1), 583-588.
[3] Ana Loboda, A. M. (2014). Discrimination of EEG- Based Motor Imagery Tasks by Means of a Simple Phase Information Method. (IJARAI) International Journal of Advanced Research in Artificial Intelligence, 3(10), 11-15.
[4] Tao Wanga, J. D. (2004). Classifying EEG-based motor imagery tasks by means of time frequency synthesized spatial patterns. Clinical Neurophysiology, 115, 2744-2753.
[5] George Townsend, B. G. (2004). Continuous EEG Classification during Motor Imagery Simulation of an Asynchronous BCI. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 12(2), 258-265.
[6] Cheolsoo Park, D. L. (2013). Classification of motor imagery BCI Using Multivariate Empirical Mode Decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(1), 10-22.
[7] Xinyang Yu, P. C.-B. (2014). Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system. Optik, 125, 1498-1502.
[8] Zhongxing Zhou, B. W. (2012). Wavelet packet-based independent component analysis for feature extraction from motor imagery EEG of complex movements. Clinical Neurophysiology, 123, 1779-1788.
[9] Phạm Phúc Ngọc, Vũ Duy Hải, Phạm Văn Bình, Nguyễn Duy Tùng, Vũ Thị Hạnh, Nguyễn Đức Thuận (2015). Developement of features set for classification of imagery hand movement - related EEG signals. Tap chí Khoa Học & Công Nghệ các Trường Đại học Kỹ thuật. 109.
[10] Nuri F. Ince, F. G. (2009). Adapting subject specific motor imagery EEG patterns in space-time-frequency for a brain computer interface. Biomedical Signal Processing and Control, 4, 236-246.
[11] Schalk, G. M. (2004). BC12000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Transactions on Biomedical Engineering, 51(6), 1034-1043.
[12] Saugat Bhattachryya, Amit Konar, D.N Tibarewala (2014). A differential evolution based energy trajectory planner for artificial limb control using motor imagery EEG signal. Biomedical Signal Processing and Control, 11, 107-113.
[13] Mohammad H. Alomari, A. S. (2013). Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning. International Journal of Advanced Computer Science and Applications,, 4(6), 207-212.
[14] S.S. Keerthi, C. L. (2003). Asymptotic behavior of support vector machines with Gaussian Kernel. Neural Comput, 15(7), 1667-1689.