EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study
IntroductionActive rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate moveme...
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1305850/full |
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| author | Runlin Dong Xiaodong Zhang Xiaodong Zhang Hanzhe Li Gilbert Masengo Aibin Zhu Aibin Zhu Xiaojun Shi Chen He |
| author_facet | Runlin Dong Xiaodong Zhang Xiaodong Zhang Hanzhe Li Gilbert Masengo Aibin Zhu Aibin Zhu Xiaojun Shi Chen He |
| author_sort | Runlin Dong |
| collection | DOAJ |
| description | IntroductionActive rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG.MethodsFirstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia.ResultsSimulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention.DiscussionThe proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users. |
| first_indexed | 2024-03-08T09:42:09Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-08T09:42:09Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-cb0c1ce257b7463088721c19c80cd6962024-01-30T04:12:50ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-01-011710.3389/fnins.2023.13058501305850EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary studyRunlin Dong0Xiaodong Zhang1Xiaodong Zhang2Hanzhe Li3Gilbert Masengo4Aibin Zhu5Aibin Zhu6Xiaojun Shi7Chen He8School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaShaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaShaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaGeneral Department, AVIC Creative Robotics Co., Ltd., Xi’an, Shaanxi, ChinaIntroductionActive rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG.MethodsFirstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia.ResultsSimulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention.DiscussionThe proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.https://www.frontiersin.org/articles/10.3389/fnins.2023.1305850/fullmovement intentionelectroencephalogramvirtual reality inductionmovement-related cortical potentialevent-related desynchronizationbrain-computer interface |
| spellingShingle | Runlin Dong Xiaodong Zhang Xiaodong Zhang Hanzhe Li Gilbert Masengo Aibin Zhu Aibin Zhu Xiaojun Shi Chen He EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study Frontiers in Neuroscience movement intention electroencephalogram virtual reality induction movement-related cortical potential event-related desynchronization brain-computer interface |
| title | EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study |
| title_full | EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study |
| title_fullStr | EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study |
| title_full_unstemmed | EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study |
| title_short | EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study |
| title_sort | eeg generation mechanism of lower limb active movement intention and its virtual reality induction enhancement a preliminary study |
| topic | movement intention electroencephalogram virtual reality induction movement-related cortical potential event-related desynchronization brain-computer interface |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1305850/full |
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