Application of Brain-Computer Interface in Post-Stroke Disease Rehabilitations
Research Article
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Application of Brain-Computer Interface in Post-Stroke Disease Rehabilitations

Rongman Wei 1*
1 School of Basic Medical Sciences, Capital Medical University, Beijing, China
*Corresponding author: rongman@mail.ccmu.edu.cn
Published on 30 July 2025
Volume Cover
TNS Vol.133
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-80590-303-1
ISBN (Online): 978-1-80590-304-8
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Abstract

Stroke is one of the leading causes of long-term disabling illness in the world, resulting in persistent functional impairment in 75% of survivors. Traditional rehabilitation methods are often limited by their inability to effectively activate long-term neuroplasticity. This study investigated the use of brain-computer interface (BCI) technology in post-stroke rehabilitation and systematically assessed its clinical efficacy for limb function recovery by analyzing neural signal decoding mechanisms and classification (invasive/non-invasive). The results of this paper showed that a motor imagery (MI)-based BCI training program enhanced neural coupling between the motor cortex and the affected limb, reflecting clinical significance in hand grasp, joint range of motion, and motor coordination. For comorbid upper and lower extremity dysfunction, the BCI intervention yielded across-the-board benefits: walking speed improved by 0.15 m/s, spasticity scores (wrists, fingers, and ankles) were significantly reduced, and Barthel Index (BI) scores improved by 5.0 points. These synchronized improvements confirm the synergistic effect of BCI on motor function, spasticity control and activities of daily living. The integrated BCI robotic system incorporates Error-Related Negativity (ERN) decoding, enabling real-time detection of motor intent errors (8.5:1 detection-to-false-alarm ratio) and precise millisecond adjustment of assistive forces. This technological advancement permits early initiation of active neuroplasticity training in acute phase patients with complete loss of voluntary movement, confirming superior efficacy compared to conventional therapy alone. By facilitating active neural remodeling, BCI technology overcomes the limitations of passive rehabilitation and offers transformative potential for chronic patients.

Keywords:

Brain-computer interface, Stroke, Rehabilitation.

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Wei,R. (2025). Application of Brain-Computer Interface in Post-Stroke Disease Rehabilitations. Theoretical and Natural Science,133,9-14.

References

[1]. Summary of the China Stroke Prevention and Control Report 2021 [J]. Chinese Journal of Cerebrovascular Disease, 2023, 20(11): 783-792

[2]. Mahidol University & University of Nebraska Medical Center. (2025). Post-discharge rehabilitation time window for stroke patients: A longitudinal cohort study. Scientific Reports.

[3]. Zhang, T., et al. (2025). Neuromodulation of motor network via high-frequency rTMS: Evidence from rs-fMRI in stroke rehabilitation. Journal of Neuroscience Methods, 25(3), 112–125.

[4]. Fujian University of Traditional Chinese Medicine. (2025). AI-driven virtual reality system for upper limb motor rehabilitation in post-stroke patients: A randomized controlled trial. Trials, 26(2), 89–101.

[5]. Smith, J., & Chen, L. (2025). Closed-loop BCI-FES for motor recovery: Synaptic plasticity and neural oscillation synchronization. Brain Research Bulletin, 152, 230–245.

[6]. Wang, Y., et al. (2025). Core stability training enhances postural control via spinal CPG activation and cerebellar-parietal integration in stroke. Neurological Sciences, 46(4), 1457–1470.

[7]. Hongyuzhang, Le Jiao, Songxiang Yang.et al. [B] Brain–computer interfaces: the innovative key to unlocking neurological conditions, 2024, 8.

[8]. MF Mridha', Sujoy Chandra Das, Muhammad Mohsin Kabir.et al [B] Brain-Computer Interface: Advancement and Challenges, 2021, 8

[9]. Iris Brunner  1, ✉,   Camilla Biering Lundquist  2,   Asger Roer Pedersen.et al. [B]Brain computer interface training with motor imagery and functional electrical stimulation for patients with severe upper limb paresis after stroke: a randomized controlled pilot trial

[10]. Xiaolu Liu, Wendong Zhang, Weibo Li.et al. [E] Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial, 2023.3

[11]. Antje Kruse, Zorica Suica, Jan Taeymans.et al. [E]Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis2020, 10

[12]. Sebastian Sieghartsleitner, Marc Sebastián-Romagosa, Woosang Cho.et al. [U]. Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system.2024.3

[13]. Isuru Senadheera, Prasad Hettiarachchi, Brendon Haslam.et al. [A]. AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.2024.10

[14]. Akshay Kumar, Lin Gao, Jiaming Li.et al. [E] Error-Related Negativity-Based Robot-Assisted Stroke Rehabilitation System: Design and Proof-of-Concept 2022, 4

References

[1]. Summary of the China Stroke Prevention and Control Report 2021 [J]. Chinese Journal of Cerebrovascular Disease, 2023, 20(11): 783-792

[2]. Mahidol University & University of Nebraska Medical Center. (2025). Post-discharge rehabilitation time window for stroke patients: A longitudinal cohort study. Scientific Reports.

[3]. Zhang, T., et al. (2025). Neuromodulation of motor network via high-frequency rTMS: Evidence from rs-fMRI in stroke rehabilitation. Journal of Neuroscience Methods, 25(3), 112–125.

[4]. Fujian University of Traditional Chinese Medicine. (2025). AI-driven virtual reality system for upper limb motor rehabilitation in post-stroke patients: A randomized controlled trial. Trials, 26(2), 89–101.

[5]. Smith, J., & Chen, L. (2025). Closed-loop BCI-FES for motor recovery: Synaptic plasticity and neural oscillation synchronization. Brain Research Bulletin, 152, 230–245.

[6]. Wang, Y., et al. (2025). Core stability training enhances postural control via spinal CPG activation and cerebellar-parietal integration in stroke. Neurological Sciences, 46(4), 1457–1470.

[7]. Hongyuzhang, Le Jiao, Songxiang Yang.et al. [B] Brain–computer interfaces: the innovative key to unlocking neurological conditions, 2024, 8.

[8]. MF Mridha', Sujoy Chandra Das, Muhammad Mohsin Kabir.et al [B] Brain-Computer Interface: Advancement and Challenges, 2021, 8

[9]. Iris Brunner  1, ✉,   Camilla Biering Lundquist  2,   Asger Roer Pedersen.et al. [B]Brain computer interface training with motor imagery and functional electrical stimulation for patients with severe upper limb paresis after stroke: a randomized controlled pilot trial

[10]. Xiaolu Liu, Wendong Zhang, Weibo Li.et al. [E] Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial, 2023.3

[11]. Antje Kruse, Zorica Suica, Jan Taeymans.et al. [E]Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis2020, 10

[12]. Sebastian Sieghartsleitner, Marc Sebastián-Romagosa, Woosang Cho.et al. [U]. Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system.2024.3

[13]. Isuru Senadheera, Prasad Hettiarachchi, Brendon Haslam.et al. [A]. AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.2024.10

[14]. Akshay Kumar, Lin Gao, Jiaming Li.et al. [E] Error-Related Negativity-Based Robot-Assisted Stroke Rehabilitation System: Design and Proof-of-Concept 2022, 4

Cite this article

Wei,R. (2025). Application of Brain-Computer Interface in Post-Stroke Disease Rehabilitations. Theoretical and Natural Science,133,9-14.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICBioMed 2025 Symposium: AI for Healthcare: Advanced Medical Data Analytics and Smart Rehabilitation

ISBN: 978-1-80590-303-1(Print) / 978-1-80590-304-8(Online)
Editor: Alan Wang
Conference website: https://2025.icbiomed.org/auckland.html
Conference date: 17 October 2025
Series: Theoretical and Natural Science
Volume number: Vol.133
ISSN: 2753-8818(Print) / 2753-8826(Online)

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