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Enhanced Motor Imagery Based Brain-Computer Interface via Vibration Stimulation and Robotic Glove for Post-Stroke Rehabilitation

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1963))

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Abstract

Motor imagery based brain-computer interface (MI-BCI) has been extensively researched as a potential intervention to enhance motor function for post-stroke patients. However, the difficulties in performing imagery tasks and the constrained spatial resolution of electroencephalography complicate the decoding of fine motor imagery (MI). To overcome the limitation, an enhanced MI-BCI rehabilitation system based on vibration stimulation and robotic glove is proposed in this paper. First, a virtual scene involving object-oriented palmar grasping and pinching actions, is designed to enhance subjects’ engagement in performing MI tasks by providing straightforward and specific goals. Then, vibration stimulation, which can offer proprioceptive feedback, is introduced to help subjects better switch their attention to the corresponding MI limbs. Finally, the self-designed pneumatic manipulator control module is developed for motion execution based on the MI classification results. Seven healthy individuals were recruited to validate the feasibility of the system in improving subjects’ MI abilities. The results show that the classification accuracy of three-class fine MI can be improved to 65.67%, which is significantly higher than the state-of-the art studies. This demonstrates the great potential of the proposed system in the application of post-stroke rehabilitation training.

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References

  1. Achanccaray, D., Izumi, S.I., Hayashibe, M.: Visual-electrotactile stimulation feedback to improve immersive brain-computer interface based on hand motor imagery. Comput. Intell. Neurosci. 2021, e8832686 (2021). https://doi.org/10.1155/2021/8832686

    Article  Google Scholar 

  2. Biasiucci, A., et al.: Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat. Commun. 9(1), 2421 (2018). https://doi.org/10.1038/s41467-018-04673-z

    Article  Google Scholar 

  3. Chen, W., et al.: Soft exoskeleton with fully actuated thumb movements for grasping assistance. IEEE Trans. Rob. 38(4), 2194–2207 (2022). https://doi.org/10.1109/TRO.2022.3148909

    Article  MathSciNet  Google Scholar 

  4. Cheng, N., et al.: Brain-computer interface-based soft robotic glove rehabilitation for stroke. IEEE Trans. Biomed. Eng. 67(12), 3339–3351 (2020)

    Article  Google Scholar 

  5. Cheng, N., et al.: Brain-computer interface-based soft robotic glove rehabilitation for stroke. IEEE Trans. Biomed. Eng. 67(12), 3339–3351 (2020). https://doi.org/10.1109/TBME.2020.2984003

    Article  Google Scholar 

  6. Costa, A.P., Møller, J.S., Iversen, H.K., Puthusserypady, S.: An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm. Comput. Biol. Med. 103, 24–33 (2018)

    Article  Google Scholar 

  7. Jochumsen, M., Niazi, I.K., Taylor, D., Farina, D., Dremstrup, K.: Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG. J. Neural Eng. 12(5), 056013 (2015)

    Article  Google Scholar 

  8. Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J. Neural Eng. 15(5), 056013 (2018). https://doi.org/10.1088/1741-2552/aace8c

    Article  Google Scholar 

  9. Liang, S., Choi, K.S., Qin, J., Pang, W.M., Wang, Q., Heng, P.A.: Improving the discrimination of hand motor imagery via virtual reality based visual guidance. Comput. Methods Programs Biomed. 132, 63–74 (2016). https://doi.org/10.1016/j.cmpb.2016.04.023

    Article  Google Scholar 

  10. Lu, B., Ge, S., Wang, H.: EEG-based classification of lower limb motor imagery with STFT and CNN. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. CCIS, vol. 1517, pp. 397–404. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92310-5_46

    Chapter  Google Scholar 

  11. Mane, R., Wu, Z., Wang, D.: Poststroke motor, cognitive and speech rehabilitation with brain-computer interface: A perspective review. Stroke Vasc. Neurol. 7(6), 541–549 (2022). https://doi.org/10.1136/svn-2022-001506

    Article  Google Scholar 

  12. Miao, M., Zeng, H., Wang, A.: Composite and multiple kernel learning for brain computer interface. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds.) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science, vol. 10635, pp. 803–810. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70096-0_82

  13. Nojima, I., Sugata, H., Takeuchi, H., Mima, T.: Brain–computer interface training based on brain activity can induce motor recovery in patients with stroke: A meta-analysis. Neurorehabil. Neural Repair 36(2), 83–96 (2022). https://doi.org/10.1177/15459683211062895

    Article  Google Scholar 

  14. Ofner, P., Schwarz, A., Pereira, J., Müller-Putz, G.R.: Upper limb movements can be decoded from the time-domain of low-frequency EEG. PLoS ONE 12(8), e0182578 (2017). https://doi.org/10.1371/journal.pone.0182578

    Article  Google Scholar 

  15. Pfurtscheller, G., Brunner, C., Schlögl, A., Da Silva, F.L.: Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31(1), 153–159 (2006)

    Article  Google Scholar 

  16. Ren, S., Wang, W., Hou, Z.G., Liang, X., Wang, J., Shi, W.: Enhanced motor imagery based brain-computer interface via FES and VR for lower limbs. IEEE Trans. Neural Syst. Rehabil. Eng. 28(8), 1846–1855 (2020). https://doi.org/10.1109/TNSRE.2020.3001990

    Article  Google Scholar 

  17. Sanes, J.N., Donoghue, J.P., Thangaraj, V., Edelman, R.R., Warach, S.: Shared neural substrates controlling hand movements in human motor cortex. Science 268(5218), 1775–1777 (1995). https://doi.org/10.1126/science.7792606

    Article  Google Scholar 

  18. Schwarz, A., Höller, M.K., Pereira, J., Ofner, P., Müller-Putz, G.R.: Decoding hand movements from human EEG to control a robotic arm in a simulation environment. J. Neural Eng. 17(3), 036010 (2020). https://doi.org/10.1088/1741-2552/ab882e

    Article  Google Scholar 

  19. Schwarz, A., Ofner, P., Pereira, J., Sburlea, A.I., Müller-Putz, G.R.: Decoding natural reach-and-grasp actions from human EEG. J. Neural Eng. 15(1), 016005 (2018). https://doi.org/10.1088/1741-2552/aa8911

    Article  Google Scholar 

  20. Shih, J.J., Krusienski, D.J., Wolpaw, J.R.: Brain-computer interfaces in medicine. Mayo Clin. Proc. 87(3), 268–279 (2012)

    Article  Google Scholar 

  21. Tao, Y., et al.: Decoding multi-class EEG signals of hand movement using multivariate empirical mode decomposition and convolutional neural network. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2754–2763 (2022). https://doi.org/10.1109/TNSRE.2022.3208710

    Article  Google Scholar 

  22. Tidare, J., Leon, M., Xiong, N., Astrand, E.: Discriminating EEG spectral power related to mental imagery of closing and opening of hand. In: 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 307–310. IEEE (2019)

    Google Scholar 

  23. Urigüen, J.A., Garcia-Zapirain, B.: EEG artifact removal-state-of-the-art and guidelines. J. Neural Eng. 12(3), 031001 (2015)

    Article  Google Scholar 

  24. Vourvopoulos, A., Bermúdez i Badia, S.: Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J. Neuroeng. Rehabil. 13(1), 1–14 (2016)

    Google Scholar 

  25. Wang, Y., Gao, S., Gao, X.: Common spatial pattern method for channel selection in motor imagery based brain-computer interface. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 5392–5395 (2005). https://doi.org/10.1109/IEMBS.2005.1615701

  26. Wu, W., Chen, Z., Gao, X., Li, Y., Brown, E.N., Gao, S.: Probabilistic common spatial patterns for multichannel EEG analysis. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 639–653 (2015). https://doi.org/10.1109/TPAMI.2014.2330598

    Article  Google Scholar 

  27. Yang, B., Ma, J., Qiu, W., Zhu, Y., Meng, X.: A new 2-class unilateral upper limb motor imagery tasks for stroke rehabilitation training. Med. Novel Technol. Devices 13, 100100 (2022)

    Article  Google Scholar 

  28. Yong, X., Menon, C.: EEG classification of different imaginary movements within the same limb. PLoS ONE 10(4), e0121896 (2015). https://doi.org/10.1371/journal.pone.0121896

    Article  Google Scholar 

  29. Zhang, W., Song, A., Lai, J.: Motor imagery BCI-based online control soft glove rehabilitation system with vibrotactile stimulation. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol. 1792, pp. 456–466. Springer, Singapore (2023). https://doi.org/10.1007/978-981-99-1642-9_39

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (Grants 1720106012, U1913601, and 62203440), Beijing Sci &Tech Program (Grant Z211100007921021), Beijing Natural Science Foundation (Grant 4202074), and ANSO Collaborative Research Project (Grant ANSO-CR-PP-2020-03).

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Authors and Affiliations

  1. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Jianqiang Su, Yihan Wang & Zeng-Guang Hou

  2. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing, 100190, China

    Jianqiang Su, Jiaxing Wang, Weiqun Wang, Yihan Wang & Zeng-Guang Hou

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  1. Jianqiang Su
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  2. Jiaxing Wang
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  5. Zeng-Guang Hou
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Corresponding authors

Correspondence to Jiaxing Wang or Zeng-Guang Hou .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Su, J., Wang, J., Wang, W., Wang, Y., Hou, ZG. (2024). Enhanced Motor Imagery Based Brain-Computer Interface via Vibration Stimulation and Robotic Glove for Post-Stroke Rehabilitation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1963. Springer, Singapore. https://doi.org/10.1007/978-981-99-8138-0_26

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  • DOI: https://doi.org/10.1007/978-981-99-8138-0_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8137-3

  • Online ISBN: 978-981-99-8138-0

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