IEEE Trans Neural Syst Rehabil Eng. 2018 Jul;26(7):1314-1323. doi: 10.1109/TNSRE.2018.2848222.
A canonical correlation analysis (CCA) is a state-of-the-art method for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. Various extended methods have been developed, and among such methods, a combination method of CCA and individual-template-based CCA has achieved the best performance. However, the CCA requires the canonical vectors to be orthogonal, which may not be a reasonable assumption for the EEG analysis. In this paper, we propose using the correlated component analysis (CORRCA) rather than CCA to implement frequency recognition. CORRCA can relax the constraint of canonical vectors in CCA and generate the same projection vector for two multichannel EEG signals. Furthermore, we propose a two-stage method based on the basic CORRCA method (termed TSCORRCA). Evaluated on a benchmark data set of 35 subjects, the experimental results demonstrate that CORRCA significantly outperformed CCA, and TSCORRCA obtained the best performance among the compared methods. This paper demonstrates that CORRCA-based methods have a great potential for implementing high-performance SSVEP-based BCI systems.
典型相关分析（CCA）是稳态视觉诱发电位（SSVEP）脑-机接口（BCI）系统中频率识别的一种先进方法。已经开发了各种扩展方法，在这些方法中，CCA 与基于个体模板的 CCA 的组合方法取得了最佳性能。然而，CCA 要求典型向量正交，这对于 EEG 分析来说可能不是一个合理的假设。在本文中，我们提出使用相关分量分析（CORRCA）而不是 CCA 来实现频率识别。CORRCA 可以放宽 CCA 中典型向量的约束，并为两个多通道 EEG 信号生成相同的投影向量。此外，我们提出了一种基于基本 CORRCA 方法（称为 TSCORRCA）的两阶段方法。在 35 名受试者的基准数据集上进行评估的实验结果表明，CORRCA 显著优于 CCA，而 TSCORRCA 在比较方法中获得了最佳性能。本文证明了基于 CORRCA 的方法在实现高性能 SSVEP 脑-机接口系统方面具有巨大的潜力。
