Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain–Computer Interfaces
Neurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/11097354/ |
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| author | Kun Wang Yuwei Liu Feifan Tian Weibo Yi Yang Zhang Tzyy-Ping Jung Minpeng Xu Dong Ming |
| author_facet | Kun Wang Yuwei Liu Feifan Tian Weibo Yi Yang Zhang Tzyy-Ping Jung Minpeng Xu Dong Ming |
| author_sort | Kun Wang |
| collection | DOAJ |
| description | Neurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that simulates real-life motor tasks to improve training efficiency. We conducted a detailed comparative analysis of the efficiency of the VR-based NFT and traditional Graz-based NFT. Forty-eight healthy subjects were randomly assigned to five groups and underwent various NFT protocols. Among them, the subjects in the four experimental groups were required to perform the NFT three times over five days, including virtual or real scenarios, as well as unilateral or bilateral hands training. We evaluated training effects by analyzing EEG features and classification performance, while online recognition duration served as the primary measure for assessing the adaptive NFT strategy. EEG analysis showed that VR-based NFT significantly enhanced the Event-related desynchronization (ERD) activations in the sensorimotor cortices over five days. The VR-based NFT group achieved a classification accuracy of 81.85%, representing a 10.14% improvement from baseline, which exceeded the 6.43% increase observed in the Graz-based NFT group. Furthermore, implementing the adaptive NFT strategy reduced the mean task duration by over 30% compared to the fixed-time training protocol. The results demonstrated that the adaptive MI-BCI-based NFT in a VR game achieves superior training outcomes while reducing training duration. These findings suggest the promising potential for applying MI-BCI NFT with VR games in motor rehabilitation following a stroke. |
| format | Article |
| id | doaj-art-e6b8df2d6e3542b0b32896f2e614ef00 |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-e6b8df2d6e3542b0b32896f2e614ef002025-08-20T04:00:34ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01332956296610.1109/TNSRE.2025.359298811097354Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain–Computer InterfacesKun Wang0https://orcid.org/0000-0002-9365-5107Yuwei Liu1https://orcid.org/0009-0006-9667-0341Feifan Tian2Weibo Yi3https://orcid.org/0000-0002-7122-1172Yang Zhang4https://orcid.org/0000-0002-3086-2432Tzyy-Ping Jung5https://orcid.org/0000-0002-8377-2166Minpeng Xu6https://orcid.org/0000-0001-6746-4828Dong Ming7https://orcid.org/0000-0002-8192-2538Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, ChinaBeijing Institute of Mechanical Equipment, Beijing, ChinaRehabilitation and Physical Therapy Department, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, Shandong, ChinaAcademy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, ChinaNeurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that simulates real-life motor tasks to improve training efficiency. We conducted a detailed comparative analysis of the efficiency of the VR-based NFT and traditional Graz-based NFT. Forty-eight healthy subjects were randomly assigned to five groups and underwent various NFT protocols. Among them, the subjects in the four experimental groups were required to perform the NFT three times over five days, including virtual or real scenarios, as well as unilateral or bilateral hands training. We evaluated training effects by analyzing EEG features and classification performance, while online recognition duration served as the primary measure for assessing the adaptive NFT strategy. EEG analysis showed that VR-based NFT significantly enhanced the Event-related desynchronization (ERD) activations in the sensorimotor cortices over five days. The VR-based NFT group achieved a classification accuracy of 81.85%, representing a 10.14% improvement from baseline, which exceeded the 6.43% increase observed in the Graz-based NFT group. Furthermore, implementing the adaptive NFT strategy reduced the mean task duration by over 30% compared to the fixed-time training protocol. The results demonstrated that the adaptive MI-BCI-based NFT in a VR game achieves superior training outcomes while reducing training duration. These findings suggest the promising potential for applying MI-BCI NFT with VR games in motor rehabilitation following a stroke.https://ieeexplore.ieee.org/document/11097354/Electroencephalography (EEG)motor imagery-based brain–computer interface (MI-BCI)neurofeedback training (NFT)adaptive NFT strategyvirtual reality game |
| spellingShingle | Kun Wang Yuwei Liu Feifan Tian Weibo Yi Yang Zhang Tzyy-Ping Jung Minpeng Xu Dong Ming Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain–Computer Interfaces IEEE Transactions on Neural Systems and Rehabilitation Engineering Electroencephalography (EEG) motor imagery-based brain–computer interface (MI-BCI) neurofeedback training (NFT) adaptive NFT strategy virtual reality game |
| title | Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain–Computer Interfaces |
| title_full | Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain–Computer Interfaces |
| title_fullStr | Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain–Computer Interfaces |
| title_full_unstemmed | Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain–Computer Interfaces |
| title_short | Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain–Computer Interfaces |
| title_sort | adaptive neurofeedback training using a virtual reality game enhances motor imagery performance in brain x2013 computer interfaces |
| topic | Electroencephalography (EEG) motor imagery-based brain–computer interface (MI-BCI) neurofeedback training (NFT) adaptive NFT strategy virtual reality game |
| url | https://ieeexplore.ieee.org/document/11097354/ |
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