Canonical correlation analysis (CCA) is an effective spatial filtering algorithm widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). In existing CCA methods, training data are used for constructing templates of stimulus targets and the spatial filters are created between the template signals and a single-trial testing signal. The fact that spatial filters rely on testing data, however, results in low classification performance of CCA compared to other state-of-the-art algorithms such as task-related component analysis (TRCA). In this study, we proposed a novel CCA method in which spatial filters are estimated using training data only. This is achieved by using observed EEG training data and their SSVEP components as the two inputs of CCA and the objective function is optimized by averaging multiple training trials. In this case, we proved in theory that the two spatial filters estimated by the CCA are equivalent, and that the CCA and TRCA are also equivalent under certain hypotheses. A benchmark SSVEP data set from 35 subjects was used to compare the performance of the two algorithms according to different lengths of data, numbers of channels and numbers of training trials. In addition, the CCA was also compared with power spectral density analysis (PSDA). The experimental results suggest that the CCA is equivalent to TRCA if the signal-to-noise ratio of training data is high enough; otherwise, the CCA outperforms TRCA in terms of classification accuracy. The CCA is much faster than PSDA in detecting time of targets. The robustness of the training data-driven CCA to noise gives it greater potential in practical applications.

中文翻译：

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一种训练数据驱动的典型相关分析算法，用于设计空间滤波器以提高基于 SSVEP 的 BCI 的性能

典型相关分析 (CCA) 是一种有效的空间滤波算法，广泛应用于基于稳态视觉诱发电位 (SSVEP) 的脑机接口 (BCI)。在现有的 CCA 方法中，训练数据用于构建刺激目标的模板，并在模板信号和单试测试信号之间创建空间滤波器。然而，空间过滤器依赖于测试数据的事实导致 CCA 的分类性能低于其他最先进的算法，例如任务相关组件分析 (TRCA)。在这项研究中，我们提出了一种新的 CCA 方法，其中仅使用训练数据来估计空间滤波器。这是通过使用观察到的 EEG 训练数据及其 SSVEP 组件作为 CCA 的两个输入来实现的，并且通过平均多个训练试验来优化目标函数。在这种情况下，我们在理论上证明了CCA估计的两个空间滤波器是等价的，并且在某些假设下CCA和TRCA也是等价的。使用来自 35 名受试者的基准 SSVEP 数据集，根据不同的数据长度、通道数和训练试验次数来比较两种算法的性能。此外，还将CCA与功率谱密度分析（PSDA）进行了比较。实验结果表明，如果训练数据的信噪比足够高，CCA相当于TRCA；否则，CCA 在分类准确性方面优于 TRCA。CCA 在检测目标时间方面比 PSDA 快得多。训练数据驱动的 CCA 对噪声的鲁棒性使其在实际应用中具有更大的潜力。