Classification of Right and Left-Hand Movement Using Multi-Resolution Analysis Method
The aim of the brain-computer interface studies on electroencephalogram (EEG) signals containing motor imagery is to extract the effective features that will provide the highest possible classification accuracy for the detection of the desired motor movement. However, achieving this goal is difficult as the most suitable frequency band and time frame vary from subject to subject. In this study, the classification success of the two-feature data obtained from raw EEG signals and the coefficients of the multi-resolution analysis method applied to the EEG signals were analyzed comparatively. The method was applied to several EEG channels (C3, Cz and C4) signals obtained from the EEG data set belonging to the publicly available BCI competition III.
[1] C. Guger, B.Z. Allison, and G. Edlinger, Brain-Computer Interface Research: A State-of-the-Art Summary 2, Springer, New York, NY, USA, 2014.
[2] A. Vallabhaneni, T. Wang, and B. He, “Brain-computer interface,” In Neural engineering, 2005, pp.85-121.
[3] A. Constantin, and A. Danyluk, A Brain-Computer Interface for the Classification of Motor Imagery. Bachelor thesis, Williams College, USA, 2007.
[4] E.A. Curran, and M.J. Stokes, “Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems,” Brain and cognition, vol. 51(3), pp. 326-336, 2003.
[5] Y. Wang, Z. Zhang, Y. Li, X. Gao, S. Gao, and F. Yang, “BCI competition 2003-data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG,” IEEE Transactions on Biomedical Engineering, vol. 51(6), pp. 1081-1086, 2004.
[6] H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement,” IEEE transactions on rehabilitation engineering, vol. 8(4), pp. 441-446. 2000.
[7] D. Garrett, D.A. Peterson, C.W. Anderson, and M.H. Thaut, “Comparison of linear, nonlinear, and feature selection methods for EEG signal classification,” IEEE Transactions on neural systems and rehabilitation engineering, vol. 11(2), pp. 141-144, 2003.
[8] Y. Zhang, Y. Wang, J. Jin, and X. Wang, “Sparse Bayesian learning for obtaining sparsity of EEG frequency bands-based feature vectors in motor imagery classification,” International journal of neural systems, vol. 27(02), pp. 1650032, 2017.
[9] H. Wang, Y. Zhang, N.R. Waytowich, D.J. Krusienski, G. Zhou, J. Jin, ... and A. Cichocki, “Discriminative feature extraction via multivariate linear regression for SSVEP-based BCI”. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24(5), pp. 532-541, 2016.
[10] Y. Zhang, Y. Wang, G. Zhou, J. Jin, B. Wang, X. Wang, and A. Cichocki, “Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces,” Expert Systems with Applications, vol. 96, pp. 302-310, 2018.
[11] Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, “Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels,” Biomedical Signal Processing and Control, vol. 38, pp. 302-311, 2017.
[12] H. Baali, A. Khorshidtalab, M. Mesbah, and M.J. Salami, “A transform-based feature extraction approach for motor imagery tasks classification,” IEEE journal of translational engineering in health and medicine, vol. 3, pp. 1-8. 2015.
[13] S. Chaudhary, S. Taran, V. Bajaj, and A. Sengur, “Convolutional neural network-based approach towards motor imagery tasks EEG signals classification,” IEEE Sensors Journal, vol. 19(12), pp. 4494-4500, 2019.
[14] L. Demanet, and L. Ying, “Wave atoms and sparsity of oscillatory patterns,” Applied and Computational Harmonic Analysis, vol. 23(3), pp. 368-387, 2007.
[15] BCI competition III, http://www.bbci.de/competition/iii/ (last accessed 30.10.2020).
[16] BCI competition III dataset IIIa, http://www.bbci.de/competition/iii/desc_IIIa.pdf (last accessed 30.10.2020).
[1] C. Guger, B.Z. Allison, and G. Edlinger, Brain-Computer Interface Research: A State-of-the-Art Summary 2, Springer, New York, NY, USA, 2014.
[2] A. Vallabhaneni, T. Wang, and B. He, “Brain-computer interface,” In Neural engineering, 2005, pp.85-121.
[3] A. Constantin, and A. Danyluk, A Brain-Computer Interface for the Classification of Motor Imagery. Bachelor thesis, Williams College, USA, 2007.
[4] E.A. Curran, and M.J. Stokes, “Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems,” Brain and cognition, vol. 51(3), pp. 326-336, 2003.
[5] Y. Wang, Z. Zhang, Y. Li, X. Gao, S. Gao, and F. Yang, “BCI competition 2003-data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG,” IEEE Transactions on Biomedical Engineering, vol. 51(6), pp. 1081-1086, 2004.
[6] H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement,” IEEE transactions on rehabilitation engineering, vol. 8(4), pp. 441-446. 2000.
[7] D. Garrett, D.A. Peterson, C.W. Anderson, and M.H. Thaut, “Comparison of linear, nonlinear, and feature selection methods for EEG signal classification,” IEEE Transactions on neural systems and rehabilitation engineering, vol. 11(2), pp. 141-144, 2003.
[8] Y. Zhang, Y. Wang, J. Jin, and X. Wang, “Sparse Bayesian learning for obtaining sparsity of EEG frequency bands-based feature vectors in motor imagery classification,” International journal of neural systems, vol. 27(02), pp. 1650032, 2017.
[9] H. Wang, Y. Zhang, N.R. Waytowich, D.J. Krusienski, G. Zhou, J. Jin, ... and A. Cichocki, “Discriminative feature extraction via multivariate linear regression for SSVEP-based BCI”. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24(5), pp. 532-541, 2016.
[10] Y. Zhang, Y. Wang, G. Zhou, J. Jin, B. Wang, X. Wang, and A. Cichocki, “Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces,” Expert Systems with Applications, vol. 96, pp. 302-310, 2018.
[11] Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, “Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels,” Biomedical Signal Processing and Control, vol. 38, pp. 302-311, 2017.
[12] H. Baali, A. Khorshidtalab, M. Mesbah, and M.J. Salami, “A transform-based feature extraction approach for motor imagery tasks classification,” IEEE journal of translational engineering in health and medicine, vol. 3, pp. 1-8. 2015.
[13] S. Chaudhary, S. Taran, V. Bajaj, and A. Sengur, “Convolutional neural network-based approach towards motor imagery tasks EEG signals classification,” IEEE Sensors Journal, vol. 19(12), pp. 4494-4500, 2019.
[14] L. Demanet, and L. Ying, “Wave atoms and sparsity of oscillatory patterns,” Applied and Computational Harmonic Analysis, vol. 23(3), pp. 368-387, 2007.
[15] BCI competition III, http://www.bbci.de/competition/iii/ (last accessed 30.10.2020).
[16] BCI competition III dataset IIIa, http://www.bbci.de/competition/iii/desc_IIIa.pdf (last accessed 30.10.2020).
@article{"International Journal of Medical, Medicine and Health Sciences:80128", author = "Nebi Gedik", title = "Classification of Right and Left-Hand Movement Using Multi-Resolution Analysis Method", abstract = "The aim of the brain-computer interface studies on electroencephalogram (EEG) signals containing motor imagery is to extract the effective features that will provide the highest possible classification accuracy for the detection of the desired motor movement. However, achieving this goal is difficult as the most suitable frequency band and time frame vary from subject to subject. In this study, the classification success of the two-feature data obtained from raw EEG signals and the coefficients of the multi-resolution analysis method applied to the EEG signals were analyzed comparatively. The method was applied to several EEG channels (C3, Cz and C4) signals obtained from the EEG data set belonging to the publicly available BCI competition III.
", keywords = "Motor imagery, EEG, wave atom transform, k-NN. ", volume = "15", number = "1", pages = "6-4", }
