Brain-computer interfaces (BCI) based on steady-state visually evoked potentials (SSVEP) have been increasingly used in different applications, ranging from entertainment to rehabilitation. Filtering techniques are crucial to detect the SSVEP response since they can increase the accuracy of the system. Here, we present an analysis of a space-time filter based on the Minimum Variance Distortionless Response (MVDR). We have compared the performance of a BCI-SSVEP using the MVDR filter to other classical approaches: Common Average Reference (CAR) and Canonical Correlation Analysis (CCA). Moreover, we combined the CAR and MVDR techniques, totalling four filtering scenarios. Feature extraction was performed using Welch periodogram, Fast Fourier transform, and CCA (as extractor) with one and two harmonics. Feature selection was performed by forward wrappers, and a linear classifier was employed for discrimination. The main analyses were carried out over a database of ten volunteers, considering two cases: four and six visual stimuli. The results show that the BCI-SSVEP using the MVDR filter achieves the best performance among the analysed scenarios. Interestingly, the system’s accuracy using the MVDR filter is practically constant even when the number of visual stimuli was increased, whereas degradation was observed for the other techniques.

Graphical abstract

中文翻译：

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基于最小方差无失真响应的SSVEP脑机接口时空滤波器

基于稳态视觉诱发电位 (SSVEP) 的脑机接口 (BCI) 已越来越多地用于不同的应用，从娱乐到康复。过滤技术对于检测 SSVEP 响应至关重要，因为它们可以提高系统的准确性。在这里，我们提出了基于最小方差无失真响应 (MVDR) 的时空滤波器分析。我们已经将使用 MVDR 滤波器的 BCI-SSVEP 的性能与其他经典方法进行了比较：公共平均参考 (CAR) 和规范相关分析 (CCA)。此外，我们结合了 CAR 和 MVDR 技术，总共有四种过滤场景。特征提取是使用 Welch 周期图、快速傅立叶变换和 CCA（作为提取器）与一个和两个谐波进行的。特征选择由前向包装器执行，并采用线性分类器进行区分。主要分析是在 10 名志愿者的数据库上进行的，考虑了两种情况：四种和六种视觉刺激。结果表明，使用 MVDR 滤波器的 BCI-SSVEP 在分析的场景中实现了最佳性能。有趣的是，即使视觉刺激的数量增加，使用 MVDR 过滤器的系统的准确性实际上是恒定的，而其他技术则观察到退化。

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