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浙江大学学报(工学版)
浙江大学学报(工学版)  2025, Vol. 59 Issue (12): 2472-2482    DOI: 10.3785/j.issn.1008-973X.2025.12.002
电子与通信工程     
基于跨受试者邻近刺激学习的稳态视觉诱发电位信号识别
杜凡(),王勇,严军,郭红想*()
中国地质大学（武汉）　机械与电子信息学院，湖北 武汉 430074
Steady-state visual evoked potential signal recognition based on cross-subject neighboring stimulus learning
Fan DU(),Yong WANG,Jun YAN,Hongxiang GUO*()
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
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摘要：

为了解决稳态视觉诱发电位（SSVEP）脑-机接口（BCI）系统在校准试验不足时的性能限制，提出跨受试者邻近刺激学习的SSVEP信号识别方法. 该方法结合目标受试者校准试验与正余弦参考信号，通过SAME数据增强方法，将正余弦参考信号邻近刺激基波和谐波信息有效融入目标受试者任务相关信息中. 在此基础上，通过分析目标受试者内、目标受试者与源受试者之间以及目标受试者与参考信号之间的相关性，利用典型相关分析（CCA）提取信号的基波和谐波特性，并结合任务相关成分分析（TRCA）提取信号的任务相关信息，从而优化空间滤波器. 从目标受试者和源受试者中提取邻近刺激公共频率信息，最终通过模板匹配实现目标识别. 实验结果表明，在仅使用一次校准试验的情况下，所提出的方法在Benchmark和BETA数据集上的识别准确率分别达到了80.17%和70.83%，且识别时间仅为0.6 s，相较于当前最先进的跨受试者学习方法eTransRCA，分别提高了16.75、15.85个百分点. 结果证明了该方法在提高识别准确率和缩短校准试验时间方面的优势，验证了其有效性.
关键词： 脑-机接口;  稳态视觉诱发电位（SSVEP）;  邻近刺激学习;  迁移学习;  跨受试者    
Abstract:

A cross-subject neighboring stimulus learning method for steady-state visual evoked potential (SSVEP) signal recognition was proposed to address the performance limitations of SSVEP brain-computer interface (BCI) systems under conditions of insufficient calibration trials. Target-subject calibration trials were combined with sine-cosine reference signals, and the sine-cosine reference signal neighboring stimulus fundamental and harmonic information was effectively incorporated into the target-subject task-relevant information through the SAME data augmentation technique. On this basis, the fundamental and harmonic characteristics of the signal were extracted using canonical correlation analysis (CCA), and the task-related information of the signal was extracted in combination with task-related component analysis (TRCA). The spatial filter was optimized by analyzing the correlations within the target subject, between the target subject and the source subject, and between the target subject and the reference signal. Common frequency information from neighboring stimulus was extracted from both the target and source subjects, and target recognition was achieved through template matching. The experimental results showed that the proposed method achieved recognition accuracies of 80.17% and 70.83% on Benchmark and BETA datasets, respectively, using only one calibration trial, and the recognition time was only 0.6 seconds, which was an improvement of 16.75 percentage points and 15.85 percentage points, respectively, compared with the current state-of-the-art cross-subject learning method, eTransRCA. These results demonstrated the advantages of improving recognition accuracy and shortening calibration trial time, validating the effectiveness of the method.
Key words: brain-computer interface    steady-state visual evoked potential (SSVEP)    neighboring stimulus learning    transfer learning    cross-subject
收稿日期: 2024-12-18 出版日期: 2025-11-25
CLC:  TN 911.7  
基金资助: 国家自然科学基金资助项目（61973283）.
通讯作者: 郭红想     E-mail: 2498117298@qq.com;guohongxiang@cug.edu.cn
作者简介: 杜凡（2001—），男，硕士生，从事脑-机接口研究. orcid.org/0009-0002-0057-1033. E-mail：2498117298@qq.com
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杜凡
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引用本文:

杜凡,王勇,严军,郭红想. 基于跨受试者邻近刺激学习的稳态视觉诱发电位信号识别[J]. 浙江大学学报(工学版), 2025, 59(12): 2472-2482.

Fan DU,Yong WANG,Jun YAN,Hongxiang GUO. Steady-state visual evoked potential signal recognition based on cross-subject neighboring stimulus learning. Journal of ZheJiang University (Engineering Science), 2025, 59(12): 2472-2482.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.12.002    或    https://www.zjujournals.com/eng/CN/Y2025/V59/I12/2472

图 1  获取空间滤波器流程图
图 2  不同数据长度和校准试验数量下的分类准确率和ITR的比较
图 3  受试者在所提方法和eTransRCA方法下获得的特征值
图 4  多刺激学习与邻近刺激学习算法对比
图 5  不同校准试验数量和源受试者数量条件下的分类准确率
图 6  不同校准试验数量的分类准确率对比
图 7  不同算法下受试者分类正确率分布
图 8  目标刺激的邻近刺激定义
图 9  不同邻近刺激数量下的分类准确率
图 10  不同数据长度的分类准确率对比
情况相关系数选择P/%
$ {{r}}_{1} $$ {{r}}_{2} $$ \dfrac{1}{3}({{r}}_{3}+{{r}}_{4}+{{r}}_{5}) $BenchmarkBETA
(a)√√√80.1770.83
(b)√√?77.6267.74
(c)√?√70.2161.78
(d)?√√79.1368.43
表 1  不同相关系数组合的消融实验
选择方法P/%
Nt = 1Nt = 2Nt = 3Nt = 4Nt = 5
随机选择70.3476.4079.5480.1381.34
有经验选择71.4378.1580.6381.5482.82
p值************
表 2  2种源受试者选择方法的分类准确率对比
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