谐波校正与泛化的稳态视觉诱发电位检测算法

doi:10.16451/j.cnki.issn1003-6059.202503007

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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.

Key words： Steady-State Visual Evoked Potential(SSVEP) Canonical Correlation Analysis Harmonics Template Correction Task-Related Component Analysis Brain-Computer Interface

Received: 13 November 2024

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.62106049), Natural Science Foundation of Fujian Province(No.2

Corresponding Authors: LUO Tianjian, Ph.D., associate professor. His research interests include pattern recognition, brain-computer interface, and EEG signal processing.

About author:: LÜ Yanhao, Master student. His research interests include brain-computer interface, pattern recognition, and EEG signal proce-ssing.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202503007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I3/280

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