Lateral control of brain-controlled vehicle based on SVM probability output model.

IF 1.6 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-02 DOI:10.1080/10255842.2025.2484565

Hongguang Pan, Hongzheng Gao, Zesheng Liu, Xinyu Yu

引用次数: 0

Abstract

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.

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基于SVM概率输出模型的脑控车辆横向控制。

本研究利用稳态视觉诱发电位（SSVEP）接口和概率支持向量机（SVM）增强了脑控车辆（BCV）的横向控制。滤波器组 CSP（FBCSP）算法改进了脑信号解码，而乙叉拟合 SVM（SF-SVM）则通过概率指令实现了更平滑的控制。在线测试的分类准确率达到 84.03%。在车道保持任务中，SF-SVM 比标准 SVM 提高了 20% 以上的完成率，减少了脑电图的非稳态效应。概率模型优化了连续控制，显著提高了 BCV 性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

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.