Enhanced Functional Connectivity of Cortical-Subcortical Sensorimotor Areas After Brain-Controlled Supernumerary Robotic Finger Training: A Longitudinal fMRI Study

Brain computer interface (BCI) technology can decode motion intentions to manipulate supernumerary robotic finger (SRF), facilitating direct human-machine control and enhancing limb motion capabilities. However, the longitudinal neuroplasticity mechanism of BCI-controlled SRF training using the sixth finger motor imagery paradigm remains unclear. In this study, we observed concurrent changes in behavior and brain activity following 4 weeks of BCI-controlled SRF training (BCI_SRF) or sham SRF training (Sham). The results demonstrated a 132% improvement in SRF-finger opposition accuracy with BCI_SRF training compared to Sham training. Resting-state fMRI analysis revealed a significant enhancement in functional connectivity between M1_ R and Cerebellum_8_L due to BCI-SRF training. These findings suggest that active brain involvement enhances the cortical-subcortical interaction of the sensorimotor network, thereby improving human-computer interaction performance. Our research provides valuable insights for enhancing human-computer interaction and exhibits significant potential in hand rehabilitation post-stroke.

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Published
2024-12-10
DOI
https://doi.org/10.1109/robio64047.2024.10907414
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00

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article

Enhanced Functional Connectivity of Cortical-Subcortical Sensorimotor Areas After Brain-Controlled Supernumerary Robotic Finger Training: A Longitudinal fMRI Study

Shuaifei Huang, Dong Ming, Zhuang Wang, Jun Guo et al.
1 links
EEG and Brain-Computer Interfaces
article

Enhanced Functional Connectivity of Cortical-Subcortical Sensorimotor Areas After Brain-Controlled Supernumerary Robotic Finger Training: A Longitudinal fMRI Study

Shuaifei Huang, Dong Ming, Zhuang Wang, Jun Guo, Yuan Liu, Wenjin Wu, Weiguo Xu, Yuyang Wang
article en

Abstract

Brain computer interface (BCI) technology can decode motion intentions to manipulate supernumerary robotic finger (SRF), facilitating direct human-machine control and enhancing limb motion capabilities. However, the longitudinal neuroplasticity mechanism of BCI-controlled SRF training using the sixth finger motor imagery paradigm remains unclear. In this study, we observed concurrent changes in behavior and brain activity following 4 weeks of BCI-controlled SRF training (BCI_SRF) or sham SRF training (Sham). The results demonstrated a 132% improvement in SRF-finger opposition accuracy with BCI_SRF training compared to Sham training. Resting-state fMRI analysis revealed a significant enhancement in functional connectivity between M1_ R and Cerebellum_8_L due to BCI-SRF training. These findings suggest that active brain involvement enhances the cortical-subcortical interaction of the sensorimotor network, thereby improving human-computer interaction performance. Our research provides valuable insights for enhancing human-computer interaction and exhibits significant potential in hand rehabilitation post-stroke.

Tianjin University (CN), Tianjin Huanhu Hospital (CN), Tianjin Medical University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 72%
EEG and Brain-Computer Interfaces
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Enhanced Functional Connectivity of Cortical-Subcortical Sensorimotor Areas After Brain-Controlled Supernumerary Robotic Finger Training: A Longitudinal fMRI Study — Shuaifei Huang, Dong Ming, et al. · (2024) | TGRS Research Map | TGRS