Department of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, Korea.
Sensors (Basel). 2021 Feb 12;21(4):1315. doi: 10.3390/s21041315.
Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.
在基于稳态视觉诱发电位 (SSVEP) 的脑-机接口 (BCI) 研究中，有许多用于频率识别的方法，其中任务相关成分分析 (TRCA) 引起了广泛关注。与现有的 SSVEP 方法相比，基于 TRCA 的 SSVEP 方法具有更低的计算成本和更高的分类性能。尽管它很有用，但基于 TRCA 的 SSVEP 方法仍然存在一个问题，即在使用短窗口长度的 EEG 信号时，其频率识别率会降低。为了解决这个问题，我们提出了一种改进的 SSVEP 解码策略，通过两步 TRCA 来实现对窗口长度不敏感。该方法重新使用 TRCA 生成的与目标频率相对应的空间滤波器。然后，该方法通过相关个体模板和测试数据来突出目标频率的特征。为了评估该方法的性能，我们使用了一个包含 35 个受试者的基准数据集，并与其他现有的 SSVEP 方法进行了比较，结果表明该方法具有显著的优越性。这些结果表明，该方法适合作为基于 SSVEP 的 BCI 应用的有效频率识别策略。
