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通过动觉错觉增强下肢运动想象。

Enhanced lower-limb motor imagery by kinesthetic illusion.

作者信息

Wang Weizhen, Shi Bin, Wang Dong, Wang Jing, Liu Gang

机构信息

Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou, China.

出版信息

Front Neurosci. 2023 Jun 20;17:1077479. doi: 10.3389/fnins.2023.1077479. eCollection 2023.

Abstract

Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% ( < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.

摘要

基于下肢运动想象(LMI)的脑机接口(BCI)能够使偏瘫患者独立站立和行走。然而,对于不了解BCI的人(如一些中风患者)来说,其LMI能力通常较差,这限制了BCI的性能。本研究提出了一种新的LMI-BCI范式,通过对跟腱进行振动刺激诱导动觉错觉(KI),以增强LMI能力。招募了16名健康受试者进行两项研究内容:(1)为验证通过振动跟腱诱导KI的可行性并分析KI产生的脑电图特征,研究1比较了有振动刺激(V-静息)和无振动刺激(静息)时参与者在静息任务期间的主观感受和大脑活动。(2)研究2比较了有KI和无KI(KI-LMI、无-LMI)时的LMI-BCI性能,以探讨KI是否增强LMI能力。两个实验的分析方法包括分类准确率(V-静息与静息、无-LMI与静息、KI-LMI与静息、KI-LMI与V-静息)、时域特征、口头问卷、统计分析和脑功能连接分析。研究1验证了通过振动跟腱诱导KI可能是可行的,并为将KI应用于LMI-BCI范式提供了理论基础,口头问卷(问题1)以及静息任务期间振动刺激的独立效应证明了这一点。研究2的结果表明,KI增强了内侧皮质的激活并诱导了更强烈的脑电图特征,事件相关去同步化(ERD)功率、地形图分布、口头问卷(问题2和问题3)以及脑功能连接图证明了这一点。此外,KI使无-LMI/静息任务的离线准确率提高了6.88%至82.19%(<0.001)。大多数受试者的模拟在线准确率也有所提高(所有受试者的平均准确率:77.23%>75.31%,所有受试者的平均F1分数:76.4%>74.3%)。本研究的LMI-BCI范式为增强LMI能力提供了一种新方法,并加速了LMI-BCI系统的实际应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f11/10319417/84dfe36865f7/fnins-17-1077479-g001.jpg

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