Bolanos Mario Corral, Barrado Ballestero Sheyla, Puthusserypady Sadasivan
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:337-340. doi: 10.1109/EMBC46164.2021.9630838.
Canonical correlation analysis (CCA) is one of the most used algorithms in the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) systems due to its simplicity, efficiency, and robustness. Researchers have proposed modifications to CCA to improve its speed, allowing high-speed spelling and thus a more natural communication. In this work, we combine two approaches, the filter-bank (FB) approach to extract more information from the harmonics, and a range of different supervised methods which optimize the reference signals to improve the SSVEP detection. The proposed models are tested on the publicly available benchmark dataset for SSVEP-based BCIs and the results show improved performance compared to the state-of-the-art methods and, in particular, the proposed FBMwayCCA approach achieves the best results with an information transfer rate (ITR) of 134.8±8.4 bits/minute. This study indeed suggests the feasibility of combining the fundamental and harmonic SSVEP components with supervised methods in target identification to develop high-speed BCI spellers.
典型相关分析（CCA）因其简单性、高效性和稳健性，成为基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）系统中最常用的算法之一。研究人员已对CCA提出改进方法以提高其速度，实现高速拼写从而使交流更自然。在这项工作中，我们结合了两种方法，一种是滤波器组（FB）方法，用于从谐波中提取更多信息，另一种是一系列不同的监督方法，这些方法优化参考信号以改善SSVEP检测。所提出的模型在基于SSVEP的BCI的公开基准数据集上进行了测试，结果表明与现有方法相比性能有所提高，特别是所提出的FBMwayCCA方法取得了最佳结果，信息传输速率（ITR）为134.8±8.4比特/分钟。这项研究确实表明了在目标识别中将基本和谐波SSVEP成分与监督方法相结合以开发高速BCI拼写器的可行性。
