CCA-based Spatio-temporal Filtering for Enhancing SSVEP Detection
CCA-based Spatio-temporal Filtering for Enhancing SSVEP Detection
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DOI：
10.1109/bsn56160.2022.9928502
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Yue Zhang;Shengquan Xie;Zhenhong Li;Yihui Zhao;Kun Qian;Zhi-Li Zhang
中科院分区：
文献类型：
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作者：
Yue Zhang;Shengquan Xie;Zhenhong Li;Yihui Zhao;Kun Qian;Zhi-Li Zhang
脑机接口（BCI）可以为人脑与外部设备之间提供直接的通信路径。基于稳态视觉诱发电位（SSVEP）的脑机接口以其高信噪比和快速的通信速率在过去几十年里得到了广泛的探索。几种空间滤波方法已经发展用于频率检测。然而，SSVEP信号中所包含的时间知识并没有得到有效利用。在这项研究中，我们提出了一种基于典型相关分析（CCA）的时空滤波方法来改进目标分类。首先通过时间信息对训练信号和两种模板信号（即个体模板和人工正弦余弦参考）进行增广。然后通过试验将三组增强数据连接起来。在新获得的训练数据和每个模板之间执行两次CCA。经过训练的四个空间滤波器可以应用于下面的测试过程。使用公共基准数据集来评估所提出的方法与其他三种比较方法（如CCA， MsetCCA和TRCA）的性能。实验结果表明，该方法的性能得到了显著提高。本文还探讨了电极数目和训练块数目对分类准确率的影响。结果进一步证明了该方法在SSVEP检测中的有效性。
Brain-computer interface (BCI) can provide a direct communication path between the human brain and an external device. The steady-state visual evoked potential (SSVEP)-based BCI has been widely explored in the past decades due to its high signal-to-noise ratio and fast communication rate. Several spatial filtering methods have been developed for frequency detection. However the temporal knowledge contained in the SSVEP signal is not effectively utilized. In this study, we propose a canonical correlation analysis (CCA)-based spatio-temporal filtering method to improve target classification. The training signal and two types of template signals (i.e. individual template and artificial sine-cosine reference) are first augmented via temporal information. Three sets of augmented data are then concatenated by trials. The CCA is performed twice, between the newly obtained training data and each template. The trained four spatial filters can be applied in the following test process. A public benchmark dataset was used to evaluate the performance of the proposed method and the other three comparing methods, such as CCA, MsetCCA, and TRCA. The experimental results indicate that the proposed method yields significantly higher performance. This paper also explored the effects of the number of electrodes and training blocks on classification accuracy. The results further demonstrated the effectiveness of the proposed method in SSVEP detection.