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OISVM: Optimal Incremental Support Vector Machine-based EEG Classification for Brain-computer Interface Model

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Abstract

The brain-computer interface (BCI) is a field of computer science where users can interact with devices in terms of brain signals. The brain signals are mimicked from the motor cortex without straining those muscles and without visualizing motor activity. By inserting various electrodes in the subject’s head, electroencephalography (EEG) is a technique used to capture the electrical patterns produced by the brain. The primary function of the EEG-based BCI is to track neural activity in the brain and convert it into signals or decisions. This paper presents a capuchin search algorithm (CSA)-optimized incremental support vector machine (ISVM) for the interpretation and classification of EEG-based BCI. The CSA is mainly incorporated to optimize the penalty and kernel parameters of the ISVM algorithm. The main aim of this paper is to aid in improving the interaction of stroke patients via computers by monitoring their thoughts. The features (electric signals), which contribute more to the output variable (hand movements), are identified via the sparse principal component analysis (SPCA) method. The proposed model is mainly trained and tested using the Berlin brain-computer interface (BCI) competition III datasets 4a and BCI competition IV datasets. The efficiency of the proposed model in analyzing the cortical sources is identified via different performance metrics such as accuracy, precision, F-score, etc. The proposed model offers accuracy, F1-score, and recall values of 92.24, 93.12, and 93.35%, respectively, which shows its efficiency in identifying hand, foot, or tongue movements.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code Availability

Not applicable.

References

  1. Benzy VK, Vinod AP, Subasree R, Alladi S, Raghavendra K. Motor imagery hand movement direction decoding using brain computer interface to aid stroke recovery and rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2020;28(12):3051–62.

    Article  Google Scholar 

  2. Mashat MEM, Lin CT, Zhang D. Effects of task complexity on motor imagery-based brain–computer interface. IEEE Trans Neural Syst Rehabil Eng. 2019;27(10):2178–85.

    Article  Google Scholar 

  3. Wu D, Xu Y, Lu BL. Transfer learning for EEG-based brain-computer interfaces: a review of progress made since 2016. IEEE Trans Cognit Dev Syst. 2020.

  4. Deng X, Zhang B, Yu N, Liu K, Sun K. Advanced TSGL-EEGNet for motor imagery EEG-based brain-computer interfaces. IEEE Access. 2021;9:25118–30.

    Article  Google Scholar 

  5. Gaur P, Pachori RB, Wang H, Prasad G. An automatic subject specific intrinsic mode function selection for enhancing two-class EEG-based motor imagery-brain computer interface. IEEE Sens J. 2019;19(16):6938–47.

    Article  Google Scholar 

  6. Zhang K, Robinson N, Lee SW, Guan C. Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network. Neural Netw. 2019;136:1–10.

    Article  Google Scholar 

  7. Aggarwal S, Chugh N. Signal processing techniques for motor imagery brain computer interface: a review. Array. 2019;1:100003.

    Article  Google Scholar 

  8. Amin SU, Alsulaiman M, Muhammad G, Mekhtiche MA, Hossain MS. Deep learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Futur Gener Comput Syst. 2019;101:542–54.

    Article  Google Scholar 

  9. Raza H, Rathee D, Zhou SM, Cecotti H, Prasad G. Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface. Neurocomputing. 2019;343:154–66.

    Article  Google Scholar 

  10. Sun B, Zhao X, Zhang H, Bai R, Li T. EEG motor imagery classification with sparse spectrotemporal decomposition and deep learning. IEEE Trans Autom Sci Eng. 2020;18(2):541–51.

    Article  Google Scholar 

  11. Ieracitano C, Mammone N, Hussain A, Morabito FC. A novel explainable machine learning approach for EEG-based brain-computer interface systems. Neural Comput Appl. 2021;1–14.

  12. Tanveer M, Tiwari A, Choudhary R, Jalan S. Sparse pinball twin support vector machines. Appl Soft Comput. 2019;78:164–75.

    Article  Google Scholar 

  13. Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng. 2018;15(5):056013.

    Article  Google Scholar 

  14. Anjerani M, Pedram MM, Mirzarezaee M, Malekian E. Data augmentation and feature extraction using deep learning for motor imagery EEG-based brain-computer interface classification. SSRN. Retrieved November 28, 2022, from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4013194.

  15. Ko LW, Ranga SSK, Komarov O, Chen CC. Development of single-channel hybrid BCI system using motor imagery and SSVEP. J Healthc Eng. 2017.

  16. Li C, Xu J, Zhu Y, Kuang S, Qu W, Sun L. Detecting self-paced walking intention based on fNIRS technology for the development of BCI. Med Biol Eng Compu. 2020;58(5):933–41.

    Article  Google Scholar 

  17. Lee DY, Lee M, Lee SW. Decoding imagined speech based on deep metric learning for intuitive BCI communication. IEEE Trans Neural Syst Rehabil Eng. 2021;29(1363–1374):2.

    Google Scholar 

  18. Yu X, Aziz MZ, Sadiq MT, Fan Z, Xiao G. A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems. IEEE Trans Instrum Meas. 2021;70:1–12.

    Google Scholar 

  19. Wang H, Yuan Z, Cheng Q, Zhang S. Geochemical anomaly mapping using sparse principal component analysis in Jining, Inner Mongolia, China. J Geochem Explor. 2022;106936.

  20. Yuan SF, Yu YB, Li MZ, Jiang H. A direct method to Frobenius norm-based matrix regression. Int J Comput Math. 2020;97(9):1767–80.

    Article  MathSciNet  MATH  Google Scholar 

  21. Xu D, Jiang M, Hu W, Li S, Pan R, Yen GG. An online prediction approach based on incremental support vector machine for dynamic multiobjective optimization. IEEE Trans Evol Comput. 2021.

  22. Tanveer M, Shubham K, Aldhaifallah M, Ho SS. An efficient regularized K-nearest neighbor based weighted twin support vector regression. Knowl-Based Syst. 2016;2016(94):70–87.

    Article  Google Scholar 

  23. Braik M, Sheta A, Al-Hiary H. A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm. Neural Comput Appl. 2021;33(7):2515–47.

    Article  Google Scholar 

  24. Kirar JS, Agrawal RK. Optimal spatio-spectral variable size subbands filter for motor imagery brain computer interface. Procedia Computer Science. 2016;84:14–21.

    Article  Google Scholar 

  25. Tangermann M, Müller KR, Aertsen A, Birbaumer N, Braun C, Brunner C, Leeb R, Mehring C, Miller KJ, Mueller-Putz G, Nolte G. Review of the BCI competition IV. Front Neurosci. 2012;55.

  26. Richhariya B, Tanveer M. EEG signal classification using Universum support vector machine. Expert Syst Appl. 2018;106:169–82.

    Article  Google Scholar 

  27. Gupta S, Krishna KH, Pachori RB, Tanveer M. Fourier-Bessel series expansion based technique for automated classification of focal and non-focal EEG signals. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1–6). IEEE. 2018.

  28. Ganaie MA, Tanveer M, Jangir J. EEG signal classification via pinball Universum twin support vector machine. Ann Oper Res. 2022;1–42.

  29. Tanveer M, Rajani T, Rastogi R, Shao YH, Ganaie MA. Comprehensive review on twin support vector machines. Ann Oper Res. 2022;1–46.

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Authors and Affiliations

  1. Department of Computing Technologies, SRMIST, Kattankulathur, Chennai, India

    P. S. Thanigaivelu & S. S. Sridhar

  2. Department of Mechatronics Engineering, SRMIST, Kattankulathur, Chennai, India

    S. Fouziya Sulthana

Authors
  1. P. S. Thanigaivelu
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  2. S. S. Sridhar
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  3. S. Fouziya Sulthana
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Correspondence to P. S. Thanigaivelu.

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Thanigaivelu, P.S., Sridhar, S.S. & Sulthana, S.F. OISVM: Optimal Incremental Support Vector Machine-based EEG Classification for Brain-computer Interface Model. Cogn Comput 15, 888–903 (2023). https://doi.org/10.1007/s12559-023-10120-z

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  • DOI: https://doi.org/10.1007/s12559-023-10120-z

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