A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients
A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients
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基于CCA融合小波系数的SSVEP脑机接口分类算法
DOI：
10.1016/j.jneumeth.2022.109502
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发表时间：
2022-02-17
影响因子：
3
通讯作者：
Qi, Yongsheng
中科院分区：
文献类型：
--
作者：
Ma, Pengfei;Dong, Chaoyi;Qi, Yongsheng
背景：在基于稳态视觉诱发电位（SSVEP）的脑部计算机界面（BCI）的研究中，如何改善BCIS的分类精度一直是研究人员的重点。规范相关分析（CCA）由于其快速性和可扩展性而广泛用于SSVEPS的BCI系统中。但是，经典的CCA算法总是在短时间内遇到低精度的难度。新方法：对于无目标刺激，本文提出了与CCA结合的融合算法（CCA-CWT-SVM），连续的小波变换和支持向量机（SVM），以提高单个特征提取时的低分类精度使用方法。结果：该融合算法在SSVEP范式中达到了高精度和信息传输速率（ITRS），很少与现有方法和结论之间的比较：通过研究来自10名受试者的400组实验数据，结果表明，CCA-CWT-SVM的分类准确度在2 s内的分类准确性为91.76％，ITR的分类精度为48.92位/min/min/min，比标准CCA高10.88％和13.18位/分钟。与主流EEG解码算法相比，过滤库规范相关分析（FBCCA），CCA-CWT-SVM算法的分类精度和ITR也得到了改善（分别为4.45％和5.69位/min）。使用Tsinghua大学（THU）的数据集，我们还表明融合算法比经典算法更好。 CCA-CWT-SVM算法在2 s的时间窗口中获得了89.1％的精度和39.91位/分钟ITR。与CCA和FBCCA的结果显着改善（CCA：79.44％和28.23位/分钟，FBCCA：84.03％和33.4位/分钟）。因此，这项工作为设计基于SSVEP的BCI系统提供了一个实验基础，该系统在某些关键的生物医学应用中具有很高的任务分类精度。
Background: In the study of brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs), how to improve the classification accuracies of BCIs has always been the focus of researchers. Canonical correlation analysis (CCA) is widely used in BCI systems of SSVEPs because of its rapidity and scalability. However, the classical CCA algorithm always encounters the difficulty of low accuracy in a short time. New method: For targetless stimuli, this paper proposes a fusion algorithm (CCA-CWT-SVM) that is combined with CCA, a continuous wavelet transform, and a support vector machine (SVM) to improve the low classification accuracies when a single feature extraction method is used.Results: This fusion algorithm achieves high accuracies and information transfer rates (ITRs) in the SSVEP paradigm with few targets.Comparison with existing methods and conclusions: Through the study of 400 groups of experimental data from 10 subjects, the results show that CCA-CWT-SVM has a classification accuracy of 91.76% within 2 s and an ITR of 48.92 bits/min, which are 10.88% and 13.18 bits/min higher than those of the standard CCA. Compared with a mainstream EEG decoding algorithm, filter bank canonical correlation analysis (FBCCA), the classification accuracy and ITR of the CCA-CWT-SVM algorithm also improved (4.45% and 5.69 bit/min, respectively). Using a dataset from Tsinghua University (THU), we also showed that the fusion algorithm is better than the classical algorithms. The CCA-CWT-SVM algorithm obtained an 89.1% accuracy and a 39.91 bit/min ITR in a time window of 2 s. The results were significantly improved compared with those of CCA and the FBCCA (CCA: 79.44% and 28.23 bits/min, FBCCA: 84.03% and 33.4 bits/min). Hence, this work provides an experimental basis for designing an SSVEP-based BCI system with a high task classification accuracy in some crucial biomedical applications.