Work

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

Year: 2025

Type: article

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.

Source: Computer Methods in Biomechanics & Biomedical Engineering

DOI: 10.1080/10255842.2025.2484565

Authors: Hongguang Pan, Hongzheng Gao, Zesheng Liu, Xinyu Yu

Institutions: Xi'an University of Science and Technology, Shandong Shengli Vocational College, Northwestern Polytechnical University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G543949953, G6628824997, G6830418761, G8027957524