谐波校正与泛化的稳态视觉诱发电位检测算法  

Steady-State Visual Evoked Potential Detection Algorithm Based on Harmonics Correction and Generalization

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作  者:吕言豪 罗天健 L Yanhao;LUO Tianjian(College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350117;Digital Fujian Internet-of-Thing Laboratory of Environmental Monitoring,Fujian Normal University,Fuzhou 350117)

机构地区:[1]福建师范大学计算机与网络空间安全学院,福州350117 [2]福建师范大学数字福建环境监测物联网实验室,福州350117

出  处:《模式识别与人工智能》2025年第3期280-292,共13页Pattern Recognition and Artificial Intelligence

基  金:国家自然科学基金项目(No.62106049);福建省自然科学基金项目(No.2022J01655)资助。

摘  要:稳态视觉诱发电位(Steady-State Visual Evoked Potential,SSVEP)被广泛应用于设计高信息传输率(Information Transfer Rate,ITR)的脑机接口.现有SSVEP检测算法通过计算最优空间滤波器,抑制非SSVEP成分的同时提高SSVEP成分的信噪比,但严重依赖训练样本的质量,早期会出现性能衰减.为了突破该瓶颈,文中提出谐波校正与泛化的稳态视觉诱发电位检测算法.首先,通过正余弦谐波参考信号校正训练均值模板,提升SSVEP刺激呈现中前期的ITR.然后,联合任务相关成分分析算法,有效提升SSVEP刺激呈现后期的ITR.在SSVEP检测过程中,加权匹配两种类型的均值训练模板,保证在任意SSVEP呈现时期都保持较高的ITR.在两个公开的SSVEP数据集上进行的对比实验表明,文中算法的SSVEP检测准确率、ITR和计算效率都较优.此外,消融实验也证实算法对校准数据要求较低,因此该研究为设计资源受限的脑机接口提供一种新的选择.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.

关 键 词:稳态视觉诱发电位(SSVEP) 典型相关分析 谐波模板校正 任务相关成分分析 脑机接口 

分 类 号:TP391[自动化与计算机技术—计算机应用技术]

 

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