A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features.
10.1007/s12264-025-01390-8
- Author:
Mengfan LI
1
;
Qi ZHAO
2
;
Tengyu ZHANG
3
,
4
;
Jiahao GE
2
;
Jingyu WANG
2
;
Guizhi XU
5
Author Information
1. State Key Laboratory of Intelligent Power Distribution Equipment and System, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, 300131, China. mfli@hebut.edu.cn.
2. State Key Laboratory of Intelligent Power Distribution Equipment and System, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, 300131, China.
3. Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, 100176, China. zhangtengyu1985@
4. com.
5. School of Electrical Engineering, Hebei University of Technology, Tianjin, 300400, China.
- Publication Type:Journal Article
- Keywords:
Brain computer interface;
EEG;
Motor imagery;
Personalized predictor
- MeSH:
Humans;
Imagination/physiology*;
Electroencephalography/methods*;
Brain-Computer Interfaces;
Male;
Female;
Adult;
Young Adult;
Brain/physiology*;
Movement/physiology*;
Motor Activity/physiology*;
Psychomotor Performance/physiology*
- From:
Neuroscience Bulletin
2025;41(7):1198-1212
- CountryChina
- Language:English
-
Abstract:
A brain-computer interface (BCI) based on motor imagery (MI) provides additional control pathways by decoding the intentions of the brain. MI ability has great intra-individual variability, and the majority of MI-BCI systems are unable to adapt to this variability, leading to poor training effects. Therefore, prediction of MI ability is needed. In this study, we propose an MI ability predictor based on multi-frequency EEG features. To validate the performance of the predictor, a video-guided paradigm and a traditional MI paradigm are designed, and the predictor is applied to both paradigms. The results demonstrate that all subjects achieved > 85% prediction precision in both applications, with a maximum of 96%. This study indicates that the predictor can accurately predict the individuals' MI ability in different states, provide the scientific basis for personalized training, and enhance the effect of MI-BCI training.
