通过基于SSVEP的BCI的受试者无关信息增强受试者特定模型的性能
Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs
摘要：
近年来，基于稳态视觉诱发电位（SSVEP）的脑-机接口（BCI）系统因其高信息传输率和目标数量的增加而受到广泛关注。然而，基于SSVEP的方法可以在其准确性和目标检测时间方面得到改进。我们提出了一种新的方法，基于典型相关分析（CCA）的主题特定的模型和主题无关的信息，并提高BCI的性能。我们建议使用其他主题的训练数据来优化特定主题的基于CCA的模型的超参数。一个合奏版本的建议的方法也开发了一个公平的比较合奏任务相关的成分分析（TRCA）。将该方法与TRCA和扩展CCA方法进行了比较。一个公开的，35主题SSVEP基准数据集用于比较研究和性能量化的分类精度和ITR。该方法的ITR高于TRCA和扩展CCA。所提出的方法优于扩展CCA在所有条件下和TRCA的时间窗口大于0.3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，添加主题独立的信息，以主题特定的模型，可以提高性能的SSVEP为基础的脑机接口。
英文摘要：
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.
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