{"title":"Lateral control of brain-controlled vehicle based on SVM probability output model.","authors":"Hongguang Pan, Hongzheng Gao, Zesheng Liu, Xinyu Yu","doi":"10.1080/10255842.2025.2484565","DOIUrl":null,"url":null,"abstract":"<p><p>This study enhances brain-controlled vehicle (BCV) lateral control using a steady-state visual evoked potential (SSVEP) interface and probabilistic support vector machine (SVM). A filter bank CSP (FBCSP) algorithm improves brain signal decoding, while a sigmoid-fitted SVM (SF-SVM) enables smoother control through probabilistic commands. Online tests achieved 84.03% classification accuracy. In lane-keeping tasks, SF-SVM improved completion rates by over 20% compared to standard SVM, reducing EEG non-stationarity effects. The probabilistic model optimized continuous control, significantly enhancing BCV performance.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7000,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Methods in Biomechanics and Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10255842.2025.2484565","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Lateral control of brain-controlled vehicle based on SVM probability output model.
This study enhances brain-controlled vehicle (BCV) lateral control using a steady-state visual evoked potential (SSVEP) interface and probabilistic support vector machine (SVM). A filter bank CSP (FBCSP) algorithm improves brain signal decoding, while a sigmoid-fitted SVM (SF-SVM) enables smoother control through probabilistic commands. Online tests achieved 84.03% classification accuracy. In lane-keeping tasks, SF-SVM improved completion rates by over 20% compared to standard SVM, reducing EEG non-stationarity effects. The probabilistic model optimized continuous control, significantly enhancing BCV performance.
期刊介绍:
The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.