Research progress on brain mechanism of brain-computer interface technology in the upper limb motor function rehabilitation in stroke patients_Journal of Biomedical Engineering

Authors：

WU Hebi ^1,2 ,  CHEN Shugeng ¹ , JIA Jie ^1,3,4

1. Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, P. R. China;
2. Institute for Translational Brain Research, Fudan University, Shanghai 200030, P. R. China;
3. National Clinical Research Center for Aging and Medicine, Huashan Hospital, Shanghai 200040, P. R. China;
4. National Center for Neurological Disorders, Shanghai 200040, P. R. China;

Corresponding author：

CHEN Shugeng, Email: tonychshug@126.com

Keywords：

Stroke; Brain-computer interface; Upper limb motor function; Brain mechanism

DOI：

10.7507/1001-5515.202404015

Video：

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Abstract Full text Figures/Tables Video References Cited by

Stroke causes abnormality of brain physiological function and limb motor function. Brain-computer interface (BCI) connects the patient's active consciousness to an external device, so as to enhance limb motor function. Previous studies have preliminarily confirmed the efficacy of BCI rehabilitation training in improving upper limb motor function after stroke, but the brain mechanism behind it is still unclear. This paper aims to review on the brain mechanism of upper limb motor dysfunction in stroke patients and the improvement of brain function in those receiving BCI training, aiming to further explore the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function after stroke. The results of this study show that in the fields of imaging and electrophysiology, abnormal activity and connectivity have been found in stroke patients. And BCI training for stroke patients can improve their upper limb motor function by increasing the activity and connectivity of one hemisphere of the brain and restoring the balance between the bilateral hemispheres of the brain. This article summarizes the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function in stroke in both imaging and electrophysiology, and provides a reference for the clinical application and scientific research of BCI in stroke rehabilitation in the future.

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2.	Ghaziani E, Couppé C, Siersma V, et al. Electrical somatosensory stimulation in early rehabilitation of arm paresis after stroke: a randomized controlled trial. Neurorehabil Neural Repair, 2018, 32(10): 899-912.
3.	Cervera M A, Soekadar S R, Ushiba J, et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann Clin Transl Neurol, 2018, 5(5): 651-663.
4.	巫嘉陵, 高忠科. 脑机接口技术及其在神经科学中的应用[J]. 中国现代神经疾病杂志, 2021, 21(1): 3-8.
5.	Monge-pereira E, Ibañez-pereda J, Alguacil-diego I M, et al. Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review. PM R, 2017, 9(9): 918-932.
6.	李玲玲, 于莹, 贾雨琦, 等. 脑机接口对脑卒中后上肢运动功能效果的Meta分析. 中国康复理论与实践, 2021, 27(7): 765-773.
7.	万春利, 邱怀德, 王雪, 等. 脑机接口对脑卒中患者功能恢复影响的meta分析. 中国康复医学杂志, 2022, 37(11): 1535-1540,1550.
8.	Wang L, Yu C, Chen H, et al. Dynamic functional reorganization of the motor execution network after stroke. Brain, 2010, 133(4): 1224-1238.
9.	Di Pino G, Pellegrino G, Assenza G, et al. Modulation of brain plasticity in stroke: a novel model for neurorehabilitation. Nat Rev Neurol, 2014, 10(10): 597-608.
10.	王敏锐. 脑卒中者及正常人手足运动脑机制的fMRI研究. 合肥: 安徽医科大学,2013.
11.	蔡伟森. 上肢运动训练促进脑卒中后脑功能重建中枢机制的功能磁共振(fMRI)研究. 上海: 复旦大学,2010.
12.	Grefkes C, Fink G R. Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain, 2011, 134(5): 1264-1276.
13.	詹爽, 余秋蓉, 尹大志, 等. 脑卒中后手运动相关脑区正负网络连接的变化: 一项静息态fMRI的研究. 磁共振成像, 2021, 12(6): 44-50.
14.	Zhao Z, Wu J, Fan M, et al. Altered intra- and inter-network functional coupling of resting-state networks associated with motor dysfunction in stroke. Hum Brain Mapp, 2018, 39(8): 3388-3397.
15.	杨浩. 脑卒中运动功能障碍静息态fMRI研究. 上海: 华东师范大学,2019.
16.	Scholkmann F, Kleiser S, Metz A J, et al. A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. Neuroimage, 2014, 85(Pt 1): 6-27.
17.	Arun K M, Smitha K A, Sylaja P N, et al. Identifying resting-state functional connectivity changes in the motor cortex using fNIRS during recovery from stroke. Brain Topogr, 2020, 33(6): 710-719.
18.	Pirovano I, Mastropietro A, Antonacci Y, et al. Resting State EEG Directed Functional Connectivity Unveils Changes in Motor Network Organization in Subacute Stroke Patients After Rehabilitation. Front Physiol, 2022, 13: 862207.
19.	Snyder D B, Schmit B D, Hyngstrom A S, et al. Electroencephalography resting‐state networks in people with Stroke. Brain and Behavior, 2021, 11(5): e02097.
20.	Gerloff C, Bushara K, Sailer A, et al. Multimodal imaging of brain reorganization in motor areas of the contralesional hemisphere of well recovered patients after capsular stroke. Brain, 2006, 129(Pt 3): 791-808.
21.	Chen S, Shu X, Jia J, et al. Relation between sensorimotor rhythm during motor attempt/imagery and upper-limb motor impairment in stroke. Clin EEG Neurosci, 2022, 53(3): 238-247.
22.	Fu J, Jiang Z, Shu X, et al. Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study. Biomed Eng Online, 2023, 22(1): 36.
23.	Chen S, Shu X, Wang H, et al. The differences between motor attempt and motor imagery in brain-computer interface accuracy and event-related desynchronization of patients with hemiplegia. Front Neurorobot, 2021, 15: 706630.
24.	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.
25.	Finger S. Chapter 51: recovery of function: redundancy and vicariation theories. Handb Clin Neurol, 2010, 95: 833-841.
26.	Nicolas-Alonso L F, Gomez-Gil J. Brain computer interfaces, a review. Sensors (Basel), 2012, 12(2): 1211-1279.
27.	Sehm B, Perez M A, Xu B, et al. Functional neuroanatomy of mirroring during a unimanual force generation task. Cereb Cortex, 2010, 20(1): 34-45.
28.	Ramos-Murguialday A, Broetz D, Rea M, et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol, 2013, 74(1): 100-108.
29.	Caria A, Weber C, Brötz D, et al. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology, 2011, 48(4): 578-582.
30.	Young B M, Nigogosyan Z, Walton L M, et al. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng, 2014, 7: 26.
31.	Young B M, Nigogosyan Z, Walton L M, et al. Dose-response relationships using brain-computer interface technology impact stroke rehabilitation. Front Hum Neurosci, 2015, 9: 361.
32.	Young B M, Nigogosyan Z, Remsik A, et al. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Front Neuroeng, 2014, 7: 25.
33.	Zhan G, Chen S, Ji Y, et al. EEG-based brain network analysis of chronic stroke patients after BCI rehabilitation training. Front Hum Neurosci. 2022, 16: 909610.
34.	Wu Q, Yue Z, Ge Y, et al. Brain functional networks study of subacute stroke patients with upper limb dysfunction after comprehensive rehabilitation including BCI training. Front Neurol, 2020, 10: 1419.
35.	Varkuti B, Guan C, Pan Y, et al. Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair, 2013, 27(1): 53-62.
36.	Song J, Nair V A, Young B M, et al. DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology. Front Hum Neurosci, 2015, 9: 195.
37.	Remsik A B, Williams L, Gjini K, et al. Ipsilesional Mu rhythm desynchronization and changes in motor behavior following post stroke BCI intervention for motor rehabilitation. Front Neurosci, 2019, 13: 53.
38.	Pichiorri F, Morone G, Petti M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol, 2015, 77(5): 851-865.
39.	Carino-Escobar R I, Carrillo-mora P, Valdés-cristerna R, et al. Longitudinal analysis of stroke patients’ brain rhythms during an intervention with a brain-computer interface. Neural Plast, 2019, 2019: 7084618.
40.	Biasiucci A, Leeb R, Iturrate I, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun, 2018, 9(1): 2421.
41.	Leeb R, Biasiucci A, Schmidlin T, et al. BCI controlled neuromuscular electrical stimulation enables sustained motor recovery in chronic stroke victims//Proceedings of the 6th International Brain-Computer Interface Meeting, the BCI Society, 2016. DOI: 10.3217/978-3-85125-467-9-108.
42.	Ang K K, Chua K S G, Phua K S, et al. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin EEG Neurosci, 2015, 46(4): 310-320.
43.	Shu X, Chen S, Yao L, et al. Fast recognition of BCI-inefficient users using physiological features from EEG signals: a screening study of stroke patients. Front Neurosci, 2018, 12: 93.
44.	Jia J. Exploration on neurobiological mechanisms of the central-peripheral-central closed-loop rehabilitation. Front Cell Neurosci. 2022, 16: 982881.
45.	贾杰. “中枢-外周-中枢”闭环康复——脑卒中后手功能康复新理念. 中国康复医学杂志, 2016, 31(11): 1180-1182.

1. 王陇德, 彭斌, 张鸿祺, 等. 《中国脑卒中防治报告2020》概要. 中国脑血管病杂志, 2022, 19(2): 136-144.
2. Ghaziani E, Couppé C, Siersma V, et al. Electrical somatosensory stimulation in early rehabilitation of arm paresis after stroke: a randomized controlled trial. Neurorehabil Neural Repair, 2018, 32(10): 899-912.
3. Cervera M A, Soekadar S R, Ushiba J, et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann Clin Transl Neurol, 2018, 5(5): 651-663.
4. 巫嘉陵, 高忠科. 脑机接口技术及其在神经科学中的应用[J]. 中国现代神经疾病杂志, 2021, 21(1): 3-8.
5. Monge-pereira E, Ibañez-pereda J, Alguacil-diego I M, et al. Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review. PM R, 2017, 9(9): 918-932.
6. 李玲玲, 于莹, 贾雨琦, 等. 脑机接口对脑卒中后上肢运动功能效果的Meta分析. 中国康复理论与实践, 2021, 27(7): 765-773.
7. 万春利, 邱怀德, 王雪, 等. 脑机接口对脑卒中患者功能恢复影响的meta分析. 中国康复医学杂志, 2022, 37(11): 1535-1540,1550.
8. Wang L, Yu C, Chen H, et al. Dynamic functional reorganization of the motor execution network after stroke. Brain, 2010, 133(4): 1224-1238.
9. Di Pino G, Pellegrino G, Assenza G, et al. Modulation of brain plasticity in stroke: a novel model for neurorehabilitation. Nat Rev Neurol, 2014, 10(10): 597-608.
10. 王敏锐. 脑卒中者及正常人手足运动脑机制的fMRI研究. 合肥: 安徽医科大学,2013.
11. 蔡伟森. 上肢运动训练促进脑卒中后脑功能重建中枢机制的功能磁共振(fMRI)研究. 上海: 复旦大学,2010.
12. Grefkes C, Fink G R. Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain, 2011, 134(5): 1264-1276.
13. 詹爽, 余秋蓉, 尹大志, 等. 脑卒中后手运动相关脑区正负网络连接的变化: 一项静息态fMRI的研究. 磁共振成像, 2021, 12(6): 44-50.
14. Zhao Z, Wu J, Fan M, et al. Altered intra- and inter-network functional coupling of resting-state networks associated with motor dysfunction in stroke. Hum Brain Mapp, 2018, 39(8): 3388-3397.
15. 杨浩. 脑卒中运动功能障碍静息态fMRI研究. 上海: 华东师范大学,2019.
16. Scholkmann F, Kleiser S, Metz A J, et al. A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. Neuroimage, 2014, 85(Pt 1): 6-27.
17. Arun K M, Smitha K A, Sylaja P N, et al. Identifying resting-state functional connectivity changes in the motor cortex using fNIRS during recovery from stroke. Brain Topogr, 2020, 33(6): 710-719.
18. Pirovano I, Mastropietro A, Antonacci Y, et al. Resting State EEG Directed Functional Connectivity Unveils Changes in Motor Network Organization in Subacute Stroke Patients After Rehabilitation. Front Physiol, 2022, 13: 862207.
19. Snyder D B, Schmit B D, Hyngstrom A S, et al. Electroencephalography resting‐state networks in people with Stroke. Brain and Behavior, 2021, 11(5): e02097.
20. Gerloff C, Bushara K, Sailer A, et al. Multimodal imaging of brain reorganization in motor areas of the contralesional hemisphere of well recovered patients after capsular stroke. Brain, 2006, 129(Pt 3): 791-808.
21. Chen S, Shu X, Jia J, et al. Relation between sensorimotor rhythm during motor attempt/imagery and upper-limb motor impairment in stroke. Clin EEG Neurosci, 2022, 53(3): 238-247.
22. Fu J, Jiang Z, Shu X, et al. Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study. Biomed Eng Online, 2023, 22(1): 36.
23. Chen S, Shu X, Wang H, et al. The differences between motor attempt and motor imagery in brain-computer interface accuracy and event-related desynchronization of patients with hemiplegia. Front Neurorobot, 2021, 15: 706630.
24. 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.
25. Finger S. Chapter 51: recovery of function: redundancy and vicariation theories. Handb Clin Neurol, 2010, 95: 833-841.
26. Nicolas-Alonso L F, Gomez-Gil J. Brain computer interfaces, a review. Sensors (Basel), 2012, 12(2): 1211-1279.
27. Sehm B, Perez M A, Xu B, et al. Functional neuroanatomy of mirroring during a unimanual force generation task. Cereb Cortex, 2010, 20(1): 34-45.
28. Ramos-Murguialday A, Broetz D, Rea M, et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol, 2013, 74(1): 100-108.
29. Caria A, Weber C, Brötz D, et al. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology, 2011, 48(4): 578-582.
30. Young B M, Nigogosyan Z, Walton L M, et al. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng, 2014, 7: 26.
31. Young B M, Nigogosyan Z, Walton L M, et al. Dose-response relationships using brain-computer interface technology impact stroke rehabilitation. Front Hum Neurosci, 2015, 9: 361.
32. Young B M, Nigogosyan Z, Remsik A, et al. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Front Neuroeng, 2014, 7: 25.
33. Zhan G, Chen S, Ji Y, et al. EEG-based brain network analysis of chronic stroke patients after BCI rehabilitation training. Front Hum Neurosci. 2022, 16: 909610.
34. Wu Q, Yue Z, Ge Y, et al. Brain functional networks study of subacute stroke patients with upper limb dysfunction after comprehensive rehabilitation including BCI training. Front Neurol, 2020, 10: 1419.
35. Varkuti B, Guan C, Pan Y, et al. Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair, 2013, 27(1): 53-62.
36. Song J, Nair V A, Young B M, et al. DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology. Front Hum Neurosci, 2015, 9: 195.
37. Remsik A B, Williams L, Gjini K, et al. Ipsilesional Mu rhythm desynchronization and changes in motor behavior following post stroke BCI intervention for motor rehabilitation. Front Neurosci, 2019, 13: 53.
38. Pichiorri F, Morone G, Petti M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol, 2015, 77(5): 851-865.
39. Carino-Escobar R I, Carrillo-mora P, Valdés-cristerna R, et al. Longitudinal analysis of stroke patients’ brain rhythms during an intervention with a brain-computer interface. Neural Plast, 2019, 2019: 7084618.
40. Biasiucci A, Leeb R, Iturrate I, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun, 2018, 9(1): 2421.
41. Leeb R, Biasiucci A, Schmidlin T, et al. BCI controlled neuromuscular electrical stimulation enables sustained motor recovery in chronic stroke victims//Proceedings of the 6th International Brain-Computer Interface Meeting, the BCI Society, 2016. DOI: 10.3217/978-3-85125-467-9-108.
42. Ang K K, Chua K S G, Phua K S, et al. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin EEG Neurosci, 2015, 46(4): 310-320.
43. Shu X, Chen S, Yao L, et al. Fast recognition of BCI-inefficient users using physiological features from EEG signals: a screening study of stroke patients. Front Neurosci, 2018, 12: 93.
44. Jia J. Exploration on neurobiological mechanisms of the central-peripheral-central closed-loop rehabilitation. Front Cell Neurosci. 2022, 16: 982881.
45. 贾杰. “中枢-外周-中枢”闭环康复——脑卒中后手功能康复新理念. 中国康复医学杂志, 2016, 31(11): 1180-1182.

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Latest ArticlesResearch progress on brain mechanism of brain-computer interface technology in the upper limb motor function rehabilitation in stroke patients

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