Steady-State Visual Evoked Potential Detection Algorithm Based on Harmonics Correction and Generalization
LÜ Yanhao1,2, LUO Tianjian1,2
1. College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117; 2. Digital Fujian Internet-of-Thing Laboratory of Environmental Monitoring, Fujian Normal University, Fuzhou 350117
Abstract：Steady-state visual evoked potential(SSVEP) is widely utilized in the design of brain-computer interfaces with high information transfer rates(ITR). Existing SSVEP detection algorithms enhance SSVEP components with high signal-noise ratios while suppressing non-SSVEP components by computing optimal spatial filters. However, these algorithms heavily depend on the quality of training samples, resulting in performance degradation in the early stage of SSVEP presentation. To overcome this limitation, a steady-state visual evoked potential detection algorithm based on harmonics correction and generalization(HCG) is proposed. First, the average training templates are corrected by the sine-cosine harmonics reference signals to enhance ITR during the early-middle stage of SSVEP presentation. Subsequently, the task-related component analysis is employed to enhance ITR during the latter stage of SSVEP presentation. For SSVEP detection, the average training templates are weighted and matched by these two types to ensure high ITR during all stages of SSVEP presentation. Comparative experiments are conducted on two public SSVEP datasets. Experiments demonstrate that HCG outperforms the current benchmark algorithms in terms of detection accuracy and ITR, as well as computational efficiency. Moreover, ablation experiments confirm that the proposed algorithm meets lower calibration data requirements, providing a new solution for the design of resource-constrained brain-computer interfaces.
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2025, Vol. 38 
