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An overview of methods of left and right foot motor imagery based on Tikhonov regularisation common spatial pattern

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Abstract

The motor imagery brain–computer interface (MI-BCI) provides an interactive control channel for spinal cord injury patients. However, the limitations of feature extraction algorithms may lead to low accuracy and instability in decoding electroencephalogram (EEG) signals. In this study, we examined the classification performance of an MI-BCI system by focusing on the distinction of the left and right foot kinaesthetic motor imagery tasks in five subjects. Feature extraction was performed using the common space pattern (CSP) and the Tikhonov regularisation CSP (TRCSP) spatial filters. TRCSP overcomes the CSP problems of noise sensitivity and overfitting. Moreover, support vector machine (SVM) and linear discriminant analysis (LDA) were used for classification and recognition. We constructed four combined classification methods (TRCSP-SVM, TRCSP-LDA, CSP-SVM, and CSP-LDA) and evaluated them by comparing their accuracies, kappa coefficients, and receiver operating characteristic (ROC) curves. The results showed that the TRCSP-SVM method performed significantly better than others (average accuracy 97%, average kappa coefficient 0.91, and average area under ROC curve (AUC) 0.98). Using TRCSP instead of standard CSP improved accuracy by up to 10%. This study provides insights into the classification of EEG signals. The results of this study can aid lower limb MI-BCI systems in rehabilitation training.

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References

  1. Attallah O, Abougharbia J, Tamazin M, Nasser AA (2020) A BCI system based on motor imagery for assisting people with motor deficiencies in the limbs. Brain Sci 10:864. https://doi.org/10.3390/brainsci10110864

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Kraus D, Naros G, Bauer R et al (2016) Brain–robot interface driven plasticity: distributed modulation of corticospinal excitability. Neuroimage 125:522–532. https://doi.org/10.1016/j.neuroimage.2015.09.074

    Article  PubMed  Google Scholar 

  3. Simon C, Bolton DAE, Kennedy NC et al (2021) Challenges and opportunities for the future of brain-computer interface in neurorehabilitation. Front Neurosci 15:1–8. https://doi.org/10.3389/fnins.2021.699428

    Article  Google Scholar 

  4. Bhattacharyya S, Konar A, Tibarewala DN (2014) A differential evolution based energy trajectory planner for artificial limb control using motor imagery EEG signal. Biomed Signal Process Control 11:107–113. https://doi.org/10.1016/j.bspc.2014.03.001

    Article  Google Scholar 

  5. Wang X, Lu H, Shen X et al (2021) Prosthetic control system based on motor imagery. Comput Methods Biomech Biomed Eng 0:1–8 https://doi.org/10.1080/10255842.2021.1977800

  6. Ono Y, Wada K, Kurata M, Seki N (2018) Enhancement of motor-imagery ability via combined action observation and motor-imagery training with proprioceptive neurofeedback. Neuropsychologia 114:134–142. https://doi.org/10.1016/j.neuropsychologia.2018.04.016

    Article  PubMed  Google Scholar 

  7. Liang S, Choi K-S, Qin J et al (2016) Improving the discrimination of hand motor imagery via virtual reality based visual guidance. Comput Methods Programs Biomed 132:63–74. https://doi.org/10.1016/j.cmpb.2016.04.023

    Article  PubMed  Google Scholar 

  8. Rahman MA, Khanam F, Ahmad M, Uddin MS (2020) Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation. Brain Informatics 7:1–11. https://doi.org/10.1186/s40708-020-00108-y

    Article  CAS  Google Scholar 

  9. Mohamed AMA, Uçan ON, Bayat O, Duru AD (2020) Classification of resting-state status based on sample entropy and power spectrum of electroencephalography (EEG). Appl Bionics Biomech 2020:1–10. https://doi.org/10.1155/2020/8853238

    Article  Google Scholar 

  10. Kim C, Sun J, Liu D et al (2018) An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI. Med Biol Eng Comput 56:1645–1658. https://doi.org/10.1007/s11517-017-1761-4

    Article  PubMed  Google Scholar 

  11. Khalaf A, Sejdic E, Akcakaya M (2019) Common spatial pattern and wavelet decomposition for motor imagery EEG-fTCD brain-computer interface. J Neurosci Methods 320:98–106. https://doi.org/10.1016/j.jneumeth.2019.03.018

    Article  PubMed  Google Scholar 

  12. Chacon-Murguia MI, Olivas-Padilla BE, Ramirez-Quintana J (2020) A new approach for multiclass motor imagery recognition using pattern image features generated from common spatial patterns. SIViP 14:915–923. https://doi.org/10.1007/s11760-019-01623-0

    Article  Google Scholar 

  13. Aldea R, Fira M (2014) Classifications of motor imagery tasks in brain computer interface using linear discriminant analysis. International Journal of Adv Res Artif Intell 3:5–9.https://doi.org/10.14569/IJARAI.2014.030702

  14. Shen X, Wang X, Lu S, et al (2022) Research on the real-time control system of lower-limb gait movement based on motor imagery and central pattern generator. Biomed Signal Process Control 71:102803 https://doi.org/10.1016/j.bspc.2021.102803

  15. Djoufack Nkengfack LC, Tchiotsop D, Atangana R, et al (2020) EEG signals analysis for epileptic seizures detection using polynomial transforms, linear discriminant analysis and support vector machines. Biomed Signal Process Control 62:102141 https://doi.org/10.1016/j.bspc.2020.102141

  16. Petersen J, Iversen HK, Puthusserypady S (2018) Motor imagery based brain computer interface paradigm for upper limb stroke rehabilitation. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 1960–1963. https://doi.org/10.1109/EMBC.2018.8512615

  17. Rani Alex JS, Haque MA, Anand A et al (2020) A deep learning approach for robotic arm control using brain-computer interface. Int J Biol Biomed Eng 14:128–135 https://doi.org/10.46300/91011.2020.14.18

  18. Fu R, Han M, Tian Y, Shi P (2020) Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis. J Neurosci Methods 343:108833 https://doi.org/10.1016/j.jneumeth.2020.108833

  19. Shuaibu Z, Qi L (2020) Optimized DNN classification framework based on filter bank common spatial pattern (FBCSP) for motor-imagery-based BCI. Int J Comput Applic 175:16–25. https://doi.org/10.5120/ijca2020920646

    Article  Google Scholar 

  20. AnaP C, JakobS M, HelleK I, Puthusserypady S (2018) An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm. Comput Biol Med 103:24–33. https://doi.org/10.1016/j.compbiomed.2018.09.021

    Article  Google Scholar 

  21. Tang Z, Li C, Wu J et al (2019) Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI. Front Inform Technol Electron Eng 20:1087–1098. https://doi.org/10.1631/FITEE.1800083

    Article  Google Scholar 

  22. Lu, Z., Zhang X et al (2022) An asynchronous artifact-enhanced electroencephalogram based control paradigm assisted by slight facial expression. Front neurosci 16:892794 https://doi.org/10.3389/fnins.2022.892794

  23. Zhang, X., Lu Z et al (2021) Realizing the application of EEG modeling in BCI classification: based on a conditional GAN converter. Front Neurosci 15:727394 https://doi.org/10.3389/fnins.2021.727394

  24. Jin J, Miao Y, Daly I et al (2019) Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Netw 118:262–270. https://doi.org/10.1016/j.neunet.2019.07.008

    Article  PubMed  Google Scholar 

  25. Wang Z, Yu Y, Xu M et al (2019) Towards a hybrid BCI gaming paradigm based on motor imagery and SSVEP. Int J Hum-Comput Interact 35:197–205. https://doi.org/10.1080/10447318.2018.1445068

    Article  Google Scholar 

  26. Lotte F, Guan C (2011) Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng 58:355–362. https://doi.org/10.1109/TBME.2010.2082539

    Article  PubMed  Google Scholar 

  27. Ghumman MK, Singh S, Singh N, Jindal B (2021) Optimization of parameters for improving the performance of EEG-based BCI system. J Reliable Intell Environ 7:145–156. https://doi.org/10.1007/s40860-020-00117-y

    Article  Google Scholar 

  28. Li Y, Koike Y (2011) A real-time BCI with a small number of channels based on CSP. Neural Comput Appl 20:1187–1192. https://doi.org/10.1007/s00521-010-0481-6

    Article  Google Scholar 

  29. Jusas S (2019) Classification of motor imagery using a combination of user-specific band and subject-specific band for brain-computer interface. Appl Sci 9:9–10. https://doi.org/10.3390/app9234990

    Article  Google Scholar 

  30. Tariq M, Trivailo PM, Simic M (2019) Classification of left and right knee extension motor imagery using common spatial pattern for BCI applications. Procedia Comput Sci 159:2598–2606. https://doi.org/10.1016/j.procs.2019.09.256

    Article  Google Scholar 

  31. Ling CX, Huang J, Zhang H (2003) AUC: a better measure than accuracy in comparing learning algorithms. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 329–341. https://doi.org/10.1007/3-540-44886-1_25

  32. Dagdevir E, Tokmakci M (2021) Truncation thresholds based empirical mode decomposition approach for classification performance of motor imagery BCI systems. Chaos Solitons Fractals 152:111450 https://doi.org/10.1016/j.chaos.2021.111450

  33. Jia Ying Li;Li Zhao;Yan Bian (2021) Classification of lower limb motor imagination signals based on LDA and KNN. Foreign Electron Meas Technol 40:9–14. https://doi.org/10.19652/j.cnki.femt.2002388

  34. Norizadeh Cherloo M, Kashefi Amiri H, Daliri MR (2021) Ensemble regularized common spatio-spectral pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification. Comput Biol Med 135:104546 https://doi.org/10.1016/j.compbiomed.2021.104546

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Funding

This work was supported by the National Natural Science Foundation of China (Nos. 81371663 and 61534003); the ‘Six Talents’ Peaks’ Project, China (No. SWYY-116); the ‘226 Engineering’ Research Project of Nantong Government; the Opening Project of State Key Laboratory of Bioelectronics, Southeast University; and the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (No. KYCX21_3085).

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  1. Jiakai Zhang and Xuemei Wang contributed equally.

Authors and Affiliations

  1. School of Information Science and Technology, Nantong University, Nantong, 226019, China

    Jiakai Zhang, Xuemei Wang, Boyang Xu, Yan Wu, Xiongjie Lou & Xiaoyan Shen

  2. Nantong Research Institute for Advanced Communication Technologies, Nantong, 226019, China

    Xiaoyan Shen

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  1. Jiakai Zhang
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Correspondence to Xiaoyan Shen.

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Zhang, J., Wang, X., Xu, B. et al. An overview of methods of left and right foot motor imagery based on Tikhonov regularisation common spatial pattern. Med Biol Eng Comput 61, 1047–1056 (2023). https://doi.org/10.1007/s11517-023-02780-8

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