2021年至2025年脑机接口技术在脑卒中康复领域应用的文献计量分析

doi:10.3969/j.issn.1006-9771.2025.11.005

Abstract

Abstract:

Objective To analyze the application trends and research hotspots of brain-computer interface (BCI) technology in stroke rehabilitation over the past five years.

Methods Relevant literatures on the use of BCI in stroke rehabilitation published between January, 2021 and August, 2025 were retrieved from the Web of Science Core Collection database. CiteSpace 6.4.R1 was used for visualization analysis.

Results A total of 458 papers were included. The annual number of publications remained at a high level. China was the leading country in publication output, with Fudan University and Aalborg University as the top institutions. The most prolific author was Mads R. Jochumsen, while G. Pfurtscheller had the highest citation frequency. The keywords and burst terms with the highest frequency in this field were brain-computer interface, motor imagery, upper limb and deep learning.

Conclusion Over the past five years, research on BCI in stroke rehabilitation has maintained a high publication volume. The research hotspots focus on innovations in BCI algorithm technology and multidimensional validation of neural mechanisms and rehabilitation efficacy.

Key words: stroke, brain-computer interface, rehabilitation, bibliometrics

CLC Number:

R743.3

JIANG Yi, LIANG Kang, CHU Jiahao, YANG Dan, GAO Fei, LI Hanzhi, DU Xiaoxia. Application of brain-computer interface technology in stroke rehabilitation from 2021 to 2025: a bibliometric analysis[J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(11): 1279-1289.

Figures/Tables 14

Figure 1

Figure 2

Table 1

Figure 3

Table 2

Figure 4

Table 3

Figure 5

Table 4

Figure 6

Table 5

Figure 7

Table 6

Figure 8

References 35

[1]	KAWALA-STERNIUK A, BROWARSKA N, AL-BAKRI A, et al. Summary of over fifty years with brain-computer interfaces: a review[J]. Brain sciences, 2021, 11(1): 43. doi: 10.3390/brainsci11010043
[2]	KÜBLER A. The history of BCI: from a vision for the future to real support for personhood in people with locked-in syndrome[J]. Neuroethics, 2019, 13, 163-180.
[3]	CANTILLO-NEGRETE J, CARINO-ESCOBAR R I, ORTEGA-ROBLES E, et al. A comprehensive guide to BCI-based stroke neurorehabilitation interventions[J]. MethodsX, 2023, 11: 102452. doi: 10.1016/j.mex.2023.102452
[4]	VARSEHI H, FIROOZABADI S M P. An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality[J]. Neural Netw, 2021: 133: 193-206. doi: 10.1016/j.neunet.2020.11.002
[5]	POO M M, DU J L, IP N Y, et al. China brain project: basic neuroscience, brain diseases, and brain-inspired computing[J]. Neuron, 2016, 92(3): 591-596. doi: 10.1016/j.neuron.2016.10.050
[6]	BATISTIĆ L, SUŠANJ D, PINČIĆ D, et al. Motor imagery classification based on EEG sensing with visual and vibrotactile guidance[J]. Sensors, 2023, 23(11): 5064. doi: 10.3390/s23115064
[7]	YOU Y, LU C, WANG W, et al. Relative CNN-RNN: learning relative atmospheric visibility from images[J]. IEEE Transact Image Process, 2018, 28(1): 45-55. doi: 10.1109/TIP.2018.2857219
[8]	SINHA P, SAHU D, PRAKASH S, et al. A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning[J]. Scientific Reports, 2025, 15(1): 9684. doi: 10.1038/s41598-025-94500-5
[9]	TANG Y, HUANG W, CHEN C, et al. CT-DCENet: deep EEG denoising via CNN-transformer-based dual-stage collaborative ensemble learning[J]. IEEE J Biomed Health Inform, 2025, 29(6): 4095-4108. doi: 10.1109/JBHI.2025.3535592
[10]	ELASHMAWI W H, AYMAN A, ANTOUN M, et al. A comprehensive review on brain-computer interface (BCI)-based machine and deep learning algorithms for stroke rehabilitation[J]. Appl Sci, 2024, 14(14): 6347. doi: 10.3390/app14146347
[11]	TONIN A, SEMPRINI M, KIPER P, et al. Brain-computer interfaces for stroke motor rehabilitation[J]. Bioengineering, 2025, 12(8): 820. doi: 10.3390/bioengineering12080820
[12]	AL-QAYSI Z T, AHMED M A, HAMMASH N M, et al. Systematic review of training environments with motor imagery brain-computer interface: coherent taxonomy, open issues and recommendation pathway solution[J]. Health Technol, 2021, 11(4): 783-801. doi: 10.1007/s12553-021-00560-8
[13]	MA Z Z, WU J J, CAO Z, et al. Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients[J]. J Neuroengin Rehabil, 2024, 21(1): 91. doi: 10.1186/s12984-024-01387-w
[14]	ZHANG M, ZHU F, JIA F, et al. Efficacy of brain-computer interfaces on upper extremity motor function rehabilitation after stroke: a systematic review and meta-analysis[J]. NeuroRehabilitation, 2024, 54(2): 199-212. doi: 10.3233/NRE-230215
[15]	LENNON O, TONELLATO M, DEL FELICE A, et al. A systematic review establishing the current state-of-the-art, the limitations, and the DESIRED checklist in studies of direct neural interfacing with robotic gait devices in stroke rehabilitation[J]. Front Neurosci, 2020, 14: 578. doi: 10.3389/fnins.2020.00578
[16]	YEN H C, CHEN W S, JENG J S, et al. Standard early rehabilitation and lower limb transcutaneous nerve or neuromuscular electrical stimulation in acute stroke patients: a randomized controlled pilot study[J]. Clinical Rehabilitation, 2019, 33(8): 1344-1354. doi: 10.1177/0269215519841420
[17]	SEMPRINI M, LENCIONI T, HINTERLANG W, et al. User-centered design and development of TWIN-Acta: a novel control suite of the TWIN lower limb exoskeleton for the rehabilitation of persons post-stroke[J]. Front Neurosci, 2022, 16: 915707. doi: 10.3389/fnins.2022.915707
[18]	REN S, WANG W, HOU Z G, et al. Enhanced motor imagery based brain-computer interface via FES and VR for lower limbs[J]. IEEE Trans Neur Sys Rehabil Engin, 2020, 28(8): 1846-1855.
[19]	BIASIUCCI A, LEEB R, ITURRATE I, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke[J]. Nat Commun, 2018, 9(1): 2421. doi: 10.1038/s41467-018-04673-z pmid: 29925890
[20]	SAID R R, HEYAT M B B, SONG K, et al. A systematic review of virtual reality and robot therapy as recent rehabilitation technologies using EEG-brain-computer interface based on movement-related cortical potentials[J]. Biosensors, 2022, 12(12): 1134. doi: 10.3390/bios12121134
[21]	COLUCCI A, VERMEHREN M, CAVALLO A, et al. Brain-computer interface-controlled exoskeletons in clinical neurorehabilitation: ready or not?[J]. Neurorehabil Neural Repair, 2022, 36(12): 747-756. doi: 10.1177/15459683221138751
[22]	WEN D, FAN Y, HSU S H, et al. Combining brain-computer interface and virtual reality for rehabilitation in neurological diseases: a narrative review[J]. Ann Phys Rehabil Med, 2021, 64(1): 101404. doi: 10.1016/j.rehab.2020.03.015
[23]	DALY J J, WOLPAW J R. Brain-computer interfaces in neurological rehabilitation[J]. Lancet Neurol, 2008, 7(11): 1032-1043. doi: 10.1016/S1474-4422(08)70223-0 pmid: 18835541
[24]	ZIADEH H, GULYAS D, NIELSEN L D, et al. "Mine works better": examining the influence of embodiment in virtual reality on the sense of agency during a binary motor imagery task with a brain-computer interface[J]. Front Psychol, 2021, 12: 806424. doi: 10.3389/fpsyg.2021.806424
[25]	AFONSO M, SÁNCHEZ-CUESTA F, GONZÁLEZ-ZAMORANO Y, et al. Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients[J]. J Neural Eng, 2024, 21(5): 056037. doi: 10.1088/1741-2552/ad8836
[26]	NUNES J D, VOURVOPOULOS A, BLANCO-MORA D A, et al. Brain activation by a VR-based motor imagery and observation task: an fMRI study[J]. PLoS One, 2023, 18(9): e0291528. doi: 10.1371/journal.pone.0291528
[27]	KAWANO T, HATTORI N, UNO Y, et al. Large-scale phase synchrony reflects clinical status after stroke: an EEG study[J]. Neurorehabil Neural Repair, 2017, 31(6): 561-570. doi: 10.1177/1545968317697031 pmid: 28506148
[28]	YANG X, SHI X, XUE X, et al. Efficacy of robot-assisted training on rehabilitation of upper limb function in patients with stroke: a systematic review and meta-analysis[J]. Arch Phys Med Rehabil, 2023, 104(9): 1498-1513. doi: 10.1016/j.apmr.2023.02.004
[29]	WHEATON L A, SHIBASAKI H, HALLETT M. Temporal activation pattern of parietal and premotor areas related to praxis movements[J]. Clin Neurophysiol, 2005, 116(5): 1201-1212. pmid: 15826863
[30]	DE SETA V, COLAMARINO E, PICHIORRI F, et al. Brain and muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals[J]. J Neural Eng, 2024, 21(6): 066015. doi: 10.1088/1741-2552/ad8838
[31]	BRAMBILLA C, PIROVANO I, MIRA R M, et al. Combined use of EMG and EEG techniques for neuromotor assessment in rehabilitative applications: a systematic review[J]. Sensors, 2021, 21(21): 7014. doi: 10.3390/s21217014
[32]	QU G, ZHOU Z, CALHOUN V D, et al. Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks[J]. Med Image Anal, 2025, 103: 103570. doi: 10.1016/j.media.2025.103570
[33]	STÄMPFLI P, REISCHAUER C, JÄRMANN T, et al. Combining fMRI and DTI: a framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTI-based fiber tractography results[J]. Neuroimage, 2008, 39(1): 119-126. pmid: 17931889
[34]	HE B, LIU Z. Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG[J]. IEEE Rev Biomed Eng, 2008, 1: 23-40. doi: 10.1109/RBME.2008.2008233 pmid: 20634915
[35]	BUTET S, FLEURY M, DUCHÉ Q, et al. EEG-fMRI neurofeedback versus motor imagery after stroke, a randomized controlled trial[J]. J Neuroeng Rehabil, 2025, 22(1): 67. doi: 10.1186/s12984-025-01598-9

国家	发文量/n	中心性
China	197	0.00
USA	71	0.12
England	31	0.35
Japan	28	0.06
Germany	27	0.06
South Korea	23	0.00
India	21	0.00
Canada	20	0.31
Spain	17	0.57
Brazil	17	0.31

机构	发文量/n	中心性
复旦大学	14	0.18
Aalborg University	14	0.05
天津大学	12	0.00
中国科学院	12	0.28
西安交通大学	11	0.05
清华大学	11	0.25
南洋理工大学	10	0.01
Charite Universitatsmedizin Berlin	9	0.05
Free University of Berlin	9	0.05
Humboldt University of Berlin	9	0.05

作者	发文量/n	中心性
Mads R Jochumsen	10	0.00
关存太	7	0.01
Surjo R Soekadar	6	0.00
贾杰	6	0.00
王晶	6	0.00
陈树耿	6	0.00
Kai Keng Ang	6	0.01
Imran Khan Niazi	4	0.00
张瑞	4	0.00
Floriana Pichiorri	4	0.00

作者	频次/n	中心性
Pfurtscheller G	167	0.31
Kai Keng Ang	158	0.63
Ander Ramos-Murguialday	129	0.37
Biasiucci A	118	0.47
Floriana Pichiorri	108	0.65
Jonathan R Wolpaw	106	0.09
María A Cervera	104	0.50
Ravikiran Mane	92	0.10
Alexander A Frolov	77	0.09
Benjamin Blankertz	71	0.04

聚类号	节点数/n	轮廓值	主要关键词
#0	21	0.911	machine learning; brain asymmetry; action observation; artificial intelligence
#1	19	0.923	amyotrophic lateral sclerosis; heart rate variability; human; sensorimotor rhythms
#2	17	0.916	feature extraction; stroke (medical condition); motors; convolutional neural networks
#3	16	0.823	motor imagery; brain-computer interface; stroke rehabilitation; electroencephalography (EEG)
#4	16	0.950	functional near-infrared spectroscopy; effective connectivity; clinical translation
#5	15	0.913	motor imagery; brain-computer interface (BCI) ; brain-computer interface; stroke
#6	15	0.950	EEG; hemiparesis; systematic review; decoding; brain-computer interfaces
#7	15	0.899	neurofeedback, motor restoration, bimanual BCI-FES, delta alpha ratio
#8	13	0.871	electroencephalography (EEG), upper limb, brain-computer interface
#9	12	0.922	motor rehabilitation, functional electrical stimulation, continuous trajectory reconstruction
#10	12	0.831	neurological diseases, virtual reality, neuroscience, volitional control
#11	11	0.968	motor function, upper extremity, stroke, network calibration, robotic arm
#12	9	0.989	movement-related cortical potential, kinesthetic motor imagery, event-related desynchronization
#13	8	0.805	neuroprosthetic, gps tracking, community participation, intracortical brain-computer interfaces

Application of brain-computer interface technology in stroke rehabilitation from 2021 to 2025: a bibliometric analysis

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 35

Related Articles 15

Metrics

Comments

Recommended 0

关键词	频次/n	中心性
brain-computer interface	337	0.03
stroke rehabilitation	260	0.02
motor imagery	187	0.00
upper limb	104	0.06
deep learning	79	0.04
EEG	54	0.00
stimulation	36	0.04
performance	36	0.01
motor recovery	32	0.09
functional electrical stimulation	31	0.04

[1]	WEI Jingyi, WANG Xiaojing, WANG Ran, WEI Chen, MA Sai, LIU Xihua. Effect of acupuncture synchronized speech training on post-stroke motor aphasia [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(9): 1000-1008.
[2]	WANG Xiaojing, WEI Jingyi, WEI Chen, WANG Ran, MA Sai, LIU Xihua. Effect of synchronous acupuncture and articulation training on spastic dysarthria after stroke [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(9): 1009-1016.
[3]	ZHOU Xinyue, YE Ruixue, MA Yaqi, XU Ying, CAO Longyao, WANG Yulong. Researches on central post-stroke pain: a bibliometric analysis [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(9): 1038-1049.
[4]	GAO Yunhan, HOU Shanshan, WANG Xinyu, ZHU Chongtian. Effect of brain-computer interface on upper limb motor dysfunction in stroke patients based on functional near-infrared spectroscopy [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(9): 1066-1073.
[5]	GAO Fei, LIU Lixu, HU Xueyan, WU Xiaoli, YANG Lingyu, YANG Yuqi, YE Changqing, DU Xiaoxia. Effect of unilateral or bilateral transcranial direct current stimulation on post-stroke dysphagia [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(9): 993-999.
[6]	ZHANG Ziang, CHEN Jing, SHEN Mengru, GENG Zongxiao, HAN Xue, ZHAO Xu, XU Lei. Comparison of effect of different types of exercise on gait and balance for stroke patients [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(8): 896-905.
[7]	LU Dandan, YAO Jingzhi, LI Zi, WANG Kewen, SUN Xinliao, CHEN Jianmin, XU Jianwen. Physical therapy for Parkinson's disease from 2014 to 2023: a bibliometric analysis [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(8): 906-913.
[8]	WANG Xiaofeng, HU Mengqiao, WANG Yan, WEI Kun, XU Wenzhu, REN Dan, MA Ye. Effect of exoskeleton robot-assisted gait training on lower limb function after stroke and spinal cord injury: a systematic review [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(8): 914-921.
[9]	LIANG Fei, CHEN Ximei, QI Jing, WANG Shurong, LIANG Yongsheng. Functioning, vocational competency and career development of college students with disabilities based on ICF and RCF [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(8): 922-929.
[10]	DONG Yuanjun, LOU Xin, SUN Rui, WANG Wenshuai, LIU Xiaoqin, XIA Xue, YANG Yaru, WANG Zhongyan, QIU Zhuoying. Cultivating key competencies of teamwork and interprofessional practice for rehabilitation psychology professionals based on RCF [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(8): 939-946.
[11]	ZHANG Zihan, GUAN Jinzhi, HUANG Xing, ZHOU Li, ZHANG Yaxuan, ZHANG Mengyuan, CHANG Jingling. Characteristics of time-domain and time-frequency of Chinese word-picture matching task-related electroencephalogram in patients with post-stroke aphasia [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(8): 947-957.
[12]	LÜ Xueqin, ZHANG Tong, LIU Huilin, LIU Jianhua, LI Da, WANG Huawei. Effect of preoperative exercise on patients undergoing ventriculoperitoneal shunt [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(8): 958-964.
[13]	SONG Zhuman, LIU Na, LIU Yang, DING Xuemei, ZHANG Xiaoli. Mental health in parents of children with autism spectrum disorder: a bibliometric analysis from 2016 to 2025 [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(7): 790-801.
[14]	SUN Wanting, YASEN Ailipinai, GONG Xiang, XIAO Yue, GAN Zhaodan, LIU Mingjie, ZENG Lanting, MA Shuyue, LU Jun, XU Guangxu. Effect of high-frequency repetitive transcranial magnetic stimulation on upper limb function of stroke patients based on motor sequence learning [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(7): 812-821.
[15]	SHAN Lei, LIU Ying, ZHANG Xin, CHI Qianqian, ZHU Xiaomin. Effect of accelerated intermittent theta burst stimulation on post-stroke depression [J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(7): 822-829.