Recently, brain-computer interface (BCI) systems developed based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high information transfer rate (ITR) and increasing number of targets. However, SSVEP-based methods can be improved in terms of their accuracy and target detection time. We propose a new method based on canonical correlation analysis (CCA) to integrate subject-specific models and subject-independent information and enhance BCI performance. We propose to use training data of other subjects to optimize hyperparameters for CCA-based model of a specific subject. An ensemble version of the proposed method is also developed for a fair comparison with ensemble task-related component analysis (TRCA). The proposed method is compared with TRCA and extended CCA methods. A publicly available, 35-subject SSVEP benchmark dataset is used for comparison studies and performance is quantified by classification accuracy and ITR. The ITR of the proposed method is higher than those of TRCA and extended CCA. The proposed method outperforms extended CCA in all conditions and TRCA for time windows greater than 0.3 s. The proposed method also outperforms TRCA when there are limited training blocks and electrodes. This study illustrates that adding subject-independent information to subject-specific models can improve performance of SSVEP-based BCIs.

中文翻译：

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通过基于 SSVEP 的 BCI 的独立于主题的信息提高特定于主题的模型的性能

近年来，基于稳态视觉诱发电位 (SSVEP) 开发的脑机接口 (BCI) 系统因其高信息传输率 (ITR) 和不断增加的目标数量而备受关注。然而，基于 SSVEP 的方法可以在准确性和目标检测时间方面进行改进。我们提出了一种基于典型相关分析 (CCA) 的新方法，以整合特定主题的模型和独立于主题的信息并提高 BCI 性能。我们建议使用其他科目的训练数据来优化特定科目的基于 CCA 的模型的超参数。还开发了所提出方法的集成版本，以便与集成任务相关组件分析 (TRCA) 进行公平比较。将所提出的方法与 TRCA 和扩展的 CCA 方法进行比较。一个公开的，35 个主题的 SSVEP 基准数据集用于比较研究，性能通过分类准确度和 ITR 进行量化。所提出方法的 ITR 高于 TRCA 和扩展 CCA。所提出的方法在所有条件下都优于扩展 CCA 和大于 0.3 s 的时间窗口的 TRCA。当训练块和电极有限时，所提出的方法也优于 TRCA。这项研究表明，将独立于主题的信息添加到特定于主题的模型可以提高基于 SSVEP 的 BCI 的性能。3 秒。当训练块和电极有限时，所提出的方法也优于 TRCA。这项研究表明，将独立于主题的信息添加到特定于主题的模型可以提高基于 SSVEP 的 BCI 的性能。3 秒。当训练块和电极有限时，所提出的方法也优于 TRCA。这项研究表明，将独立于主题的信息添加到特定于主题的模型可以提高基于 SSVEP 的 BCI 的性能。