A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability
A brain-computer interface (BCI) based on motor imagery (MI) can translate users’ subjective movement-related mental state without external stimulus, which has been successfully used for replacing and repairing motor function. In contrast with studies about decoding methods, less work was...
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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/10835223/ |
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| _version_ | 1832592971575328768 |
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| author | Yukun Zhang Chuncheng Zhang Rui Jiang Shuang Qiu Huiguang He |
| author_facet | Yukun Zhang Chuncheng Zhang Rui Jiang Shuang Qiu Huiguang He |
| author_sort | Yukun Zhang |
| collection | DOAJ |
| description | A brain-computer interface (BCI) based on motor imagery (MI) can translate users’ subjective movement-related mental state without external stimulus, which has been successfully used for replacing and repairing motor function. In contrast with studies about decoding methods, less work was reported about training users to improve the performance of MI-BCIs. This study aimed to develop a novel MI feedback training method to enhance the ability of humans to use the MI-BCI system. In this study, an adaptive MI feedback training method was proposed to improve the effectiveness of the training process. The method updated the feedback model during training process and assigned different weights to the samples to better adapt the changes in the distribution of the Electroencephalograms (EEGs). An online feedback training system was established. Each of ten subjects participated in a three-day experiment involving three different feedback methods: no feedback algorithm update, feedback algorithm update, and feedback algorithm update using the proposed adaptive method. Comparison experiments were conducted on three different feedback methods. The experimental results showed that the feedback algorithm using the proposed method can most quickly improve the MI classification accuracy and has the largest increase in accuracy. This indicates that the proposed method can enhance the effectiveness of feedback training and improve the practicality of MI-BCI systems. |
| format | Article |
| id | doaj-art-f77cc3db584841e5b979879fbce40c52 |
| 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-f77cc3db584841e5b979879fbce40c522025-01-21T00:00:12ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-013338039010.1109/TNSRE.2025.352762910835223A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery AbilityYukun Zhang0https://orcid.org/0000-0001-8146-4592Chuncheng Zhang1Rui Jiang2Shuang Qiu3https://orcid.org/0000-0002-8327-2567Huiguang He4https://orcid.org/0000-0002-0684-1711China Mobile (Hangzhou) Information Technology Company Ltd., Hangzhou, Zhejiang, ChinaLaboratory of Brain Atlas and Brain-Inspired Intelligence, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaA brain-computer interface (BCI) based on motor imagery (MI) can translate users’ subjective movement-related mental state without external stimulus, which has been successfully used for replacing and repairing motor function. In contrast with studies about decoding methods, less work was reported about training users to improve the performance of MI-BCIs. This study aimed to develop a novel MI feedback training method to enhance the ability of humans to use the MI-BCI system. In this study, an adaptive MI feedback training method was proposed to improve the effectiveness of the training process. The method updated the feedback model during training process and assigned different weights to the samples to better adapt the changes in the distribution of the Electroencephalograms (EEGs). An online feedback training system was established. Each of ten subjects participated in a three-day experiment involving three different feedback methods: no feedback algorithm update, feedback algorithm update, and feedback algorithm update using the proposed adaptive method. Comparison experiments were conducted on three different feedback methods. The experimental results showed that the feedback algorithm using the proposed method can most quickly improve the MI classification accuracy and has the largest increase in accuracy. This indicates that the proposed method can enhance the effectiveness of feedback training and improve the practicality of MI-BCI systems.https://ieeexplore.ieee.org/document/10835223/Brain-computer interfacemotor imageryfeedback trainingdistribution adaptiveonline system |
| spellingShingle | Yukun Zhang Chuncheng Zhang Rui Jiang Shuang Qiu Huiguang He A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability IEEE Transactions on Neural Systems and Rehabilitation Engineering Brain-computer interface motor imagery feedback training distribution adaptive online system |
| title | A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability |
| title_full | A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability |
| title_fullStr | A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability |
| title_full_unstemmed | A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability |
| title_short | A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability |
| title_sort | distribution adaptive feedback training method to improve human motor imagery ability |
| topic | Brain-computer interface motor imagery feedback training distribution adaptive online system |
| url | https://ieeexplore.ieee.org/document/10835223/ |
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