Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

doi:10.1016/S1005-8885(17)60215-2

中国邮电高校学报(英文) ›› 2017, Vol. 24 ›› Issue (3): 83-90.doi: 10.1016/S1005-8885(17)60215-2

Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

李端¹,张洪欣²,Muhammad Saad Khan¹,米芳¹

1. 北京邮电大学
2. 北京邮电大学电子工程学院

收稿日期:2016-11-14 修回日期:2017-04-07 出版日期:2017-06-30 发布日期:2017-06-30
通讯作者: 张洪欣 E-mail:hongxinzhang@263.net
基金资助:
基于多源信息融合的电路芯片旁路分析方法研究

Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

Received:2016-11-14 Revised:2017-04-07 Online:2017-06-30 Published:2017-06-30
Contact: Hong-Xin ZHANG E-mail:hongxinzhang@263.net
Supported by:
Study of integrated circuit side-channel analysis based on Multi-information fusion

摘要/Abstract

摘要： Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery (MI) brain-computer interface (BCI) system. In this paper, we propose a common spatial pattern (CSP) and chaotic particle swarm optimization (CPSO) twin support vector machine (TWSVM) scheme for classification of MI electroencephalography (EEG). The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine (SVM), particle swarm optimization (PSO) TWSVM, linear discriminant analysis (LDA), back propagation (BP) and probabilistic neural network (PNN), respectively. Furthermore, it achieved a faster or comparable central processing unit (CPU) running time over the traditional SVM methods.

关键词: brain-computer interface, motor imagery, twin support vector machine, chaotic particle swarm optimization

Abstract: Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery (MI) brain-computer interface (BCI) system. In this paper, we propose a common spatial pattern (CSP) and chaotic particle swarm optimization (CPSO) twin support vector machine (TWSVM) scheme for classification of MI electroencephalography (EEG). The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine (SVM), particle swarm optimization (PSO) TWSVM, linear discriminant analysis (LDA), back propagation (BP) and probabilistic neural network (PNN), respectively. Furthermore, it achieved a faster or comparable central processing unit (CPU) running time over the traditional SVM methods.

Key words: brain-computer interface, motor imagery, twin support vector machine, chaotic particle swarm optimization

中图分类号:

TP391.4/TP391.9

参考文献

1. Kauhanen L, Nykopp T, Lehtonen J, et al. EEG and MEG brain-computer interface for tetraplegic patients. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006, 14(2): 190-193

2. Kronegg J, Chanel G, Voloshynovskiy S, et al. EEG-based synchronized braincomputer interfaces: a model for optimizing the number of mental tasks. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15(1): 50-58

3. Ahangi A, Karamnejad M, Mohammadi N, et al. Multiple classifier system for EEG signal classification with application to brain-computer interfaces. Neural Computing and Applications, 2013, 23(5): 1319-1327

4. Long J Y, Li Y Q, Yu Z L. A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces. Cognitive Neurodynanics, 2010, 4(3): 207-216

5. Kaiser V, Daly I, Pichiorri F, et al. Relationship between electrical brain responses to motor imagery and motor impairment in stroke. Stroke, 2012, 43(10): 2735-2740

6. Neuper C, Wörtz M, Pfurtscheller G. ERD/ERS patterns reflecting sensorimotor activation and deactivation. Progress in Brain Research, 2006, 159: 211-222

7. Pfurtscheller G, Brunner C, Schlögl A, et al. Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage, 2006, 31(1): 153-159

8. Hsu W Y, Sun Y N. EEG-based motor imagery analysis using weighted wavelet transform features. Journal of Neuroscience Methods, 2009, 167(2): 310-318

9. Obermaier B, Neuper C, Guger C, et al. Information transfer rate in a five-classes brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2001, 9(3): 283-288

10. Torres-García A A, Reyes-García C A, Villaseñor-Pineda L, et al. Implementing a fuzzy inference system in a multi-objective EEG channel selection model for imagined speech classification. Expert Systems with Applications, 2016, 59: 1-12

11. Burke D P, Kelly S P, de Chazal P, et al. A parametric feature extraction and classification strategy for brain-computer interfacing. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005, 13(1): 12-17

12. Mo H W, Zhao Y Y. Motor imagery electroencephalograph classification based on optimized support vector machine by magnetic bacteria optimization algorithm. Neural Processing Letters, 2016, 44(1): 185-197

13. Nicolas-Alonso L F, Corralejo R, Gomez-Pilar J, et al. Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain-computer interfaces. Neurocomputing, 2015, 159: 186-196

14. Ang K K, Chin Z Y, Wang C C, et al. Filter bank common spatial pattern algorithm on BCI competition IV dataset 2a and 2b. Fontiers in Neuroscience, 2012, DOI:10.3389/fnins.2012.00039

15. Raza H, Cecotti H, Li Y H, et al. Adaptive learning with covariate shift-detection for motor imagery-based brain-computer interface. Soft Computing, 2016, 20(8): 3085-3096

16. Krusienski D J, McFarland D J, Wolpaw J R. An evaluation of autoregressive spectral estimation model order for brain-computer interface applications. Proceedings of the 28th Annual International Conference of the Engineering in Medicine and Biology Society (EMBS’06), Aug 30-Sept 3, 2006, New York, NY, USA. Piscataway, NJ, USA: IEEE, 2006: 1323-1326

17. Lotte F, Congedo M, Lécuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering, 2007, 4(2): R1-R13

18. Suk H I, Lee S W. A novel Bayesian framework for discriminative feature extraction in brain-computer interfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(2): 286-299

19. Zhong M J, Lotte F, Girolami M, et al. Classifying EEG for brain computer interfaces using Gaussian processes. Pattern Recognition Letters, 2008, 29(3): 354-359

20. Wang Y J, Zhang Z G, Li Y, et al. BCI competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG. IEEE Transactions on Biomedical Engineering, 2004, 51(6): 1081-1086

21. Phothisonothai M, Nakagawa M. EEG-based classification of motor imagery tasks using fractal dimension and neural network for brain-computer interface. IEICE Transactions on Information and Systems, 2008, E91-D(1): 44-53

22. Coyle D, Prasa G, McGinnity T M. A time-series prediction approach for feature extraction in a brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005, 13(4): 461-467

23. Zhang J, Fan X H, Ban D K. Smooth support vector machine based on circular tangent function. The Journal of China Universities of Posts and Telecommunications, 2016, 23(1): 68-72

24. Schlögl A, Lee F, Bischof H, et al. Characterization of four-class motor imagery EEG data for the BCI-competition 2005. Journal of Neural Engineering, 2005, 2(4): L14-L22

25. Park C, Looney D, ur Rehman N, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(1): 10-22

26. Jayadeva, Khemchandani R, Chandra S. Twin support vector machines for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5): 905-910

27. Shao Y H, Zhang C H, Wang X B, et al. Improvements on twin support vector machines. IEEE Transactions on Neural Networks, 2011, 22(6): 962-968

28. Qi Z Q, Tian Y J, Shi Y. Robust twin support vector machine for pattern classification. Pattern Recognition, 2013, 46(1): 305-316

29. Tomar D, Agarwal S. Twin support vector machine: a review from 2007 to 2014. Egyptian Informatics Journal, 2015, 16(1): 55-69

30. Naik G R, Kumar D K, Jayadeva. Twin SVM for gesture classification using the surface electromyogram. IEEE Transactions on Information Technology in Biomedicine, 2010, 14(2): 301-308

31. Naik G R, Kumar D K, Jayadeva. Hybrid independent component analysis and twin support vector machine learning scheme for subtle gesture recognition. Biomedizinische Technik, 2010, 55(5): 301-307

32. Arjunan S P, Kumar D K, Naik G R. A machine learning based method for classification of fractal features of forearm sEMG using twin support vector machines. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS’10), Aug 31-Sept 4, 2010, Buenos Aires, Argentina. Piscataway, NJ, USA: IEEE, 2010: 4821-4824

33. Wang T H, Zhao D Y, Feng Y S. Two-stage multiple kernel learning with multiclass kernel polarization. Knowledge-Based Systems, 2013, 48(2): 10-16

34. Tang M Q, Xin Y L. Energy efficient power allocation in cognitive radio network using coevolution chaotic particle swarm optimization. Computer Networks, 2016, 5: 1-11

Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

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