A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features
Mengfan Li1 · Qi Zhao1 · Tengyu Zhang2 · Jiahao Ge1 · Jingyu Wang1 · Guizhi Xu31 State Key Laboratory of Intelligent Power Distribution Equipment and System, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China
2 Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Afairs, Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing 100176, China
3 School of Electrical Engineering, Hebei University of Technology, Tianjin 300400, China
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.
Keywords
EEG; Brain computer interface; Motor imagery; Personalized predictor
