{"title":"基于多频脑电图特征的运动想象能力个性化预测。","authors":"Mengfan Li, Qi Zhao, Tengyu Zhang, Jiahao Ge, Jingyu Wang, Guizhi Xu","doi":"10.1007/s12264-025-01390-8","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19314,"journal":{"name":"Neuroscience bulletin","volume":" ","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features.\",\"authors\":\"Mengfan Li, Qi Zhao, Tengyu Zhang, Jiahao Ge, Jingyu Wang, Guizhi Xu\",\"doi\":\"10.1007/s12264-025-01390-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":19314,\"journal\":{\"name\":\"Neuroscience bulletin\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuroscience bulletin\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12264-025-01390-8\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroscience bulletin","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12264-025-01390-8","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features.
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.
期刊介绍：
Neuroscience Bulletin (NB), the official journal of the Chinese Neuroscience Society, is published monthly by Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) and Springer.
NB aims to publish research advances in the field of neuroscience and promote exchange of scientific ideas within the community. The journal publishes original papers on various topics in neuroscience and focuses on potential disease implications on the nervous system. NB welcomes research contributions on molecular, cellular, or developmental neuroscience using multidisciplinary approaches and functional strategies. We feature full-length original articles, reviews, methods, letters to the editor, insights, and research highlights. As the official journal of the Chinese Neuroscience Society, which currently has more than 12,000 members in China, NB is devoted to facilitating communications between Chinese neuroscientists and their international colleagues. The journal is recognized as the most influential publication in neuroscience research in China.
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