IEEE Trans Neural Syst Rehabil Eng. 2020 Mar;28(3):552-560. doi: 10.1109/TNSRE.2020.2968307. Epub 2020 Jan 21.
The research in non-invasive Brain-Computer Interface (BCI) has led to significant improvements in the recent years for potential end users. However, the user experience and the BCI illiteracy problem remains challenging areas to address for obtaining robust and resilient clinical applications. In this study, we address the choice of the time segment for the detection of steady state visual evoked potential (SSVEP) detection. This problem has been widely addressed for the detection of event-related potentials compared to SSVEP based BCIs. The choice of this parameter is typically fixed and has a direct influence on both the detection accuracy and the information transfer rate. We propose to shift the problem of the time segment to the choice of the threshold for determining if a response has been properly detected. We consider two open-datasets for benchmarking the rationale of the approach. The results support the conclusion that an adaptive time segment for each trial, based on the selection of a threshold, can lead to a substantial higher ITR (86.92 bits/min), compared to the time segment chosen at the user (79.56 bits/min) or group level (73.78 bits/min). Finally, the results suggest that the threshold could be determined automatically in relation to the number of classes. Such an approach can leverage the literacy of SSVEP based BCI.
近年来，非侵入式脑-机接口（BCI）的研究为潜在的终端用户带来了显著的进步。然而，用户体验和 BCI 知识普及问题仍然是获得稳健和有弹性的临床应用的挑战性领域。在本研究中，我们解决了稳态视觉诱发电位（SSVEP）检测的时间段选择问题。与基于 SSVEP 的 BCI 相比，这个问题在检测事件相关电位方面已经得到了广泛的研究。这个参数的选择通常是固定的，直接影响检测精度和信息传输率。我们提出将时间段的选择问题转移到确定是否正确检测到响应的阈值选择上。我们考虑了两个公开数据集来验证该方法的合理性。结果支持这样的结论，即基于选择阈值为每个试验自适应时间段，可以显著提高信息传输率（86.92 位/分钟），与用户（79.56 位/分钟）或组水平（73.78 位/分钟）选择的时间段相比。最后，结果表明，阈值可以根据类别的数量自动确定。这种方法可以利用 SSVEP 为基础的 BCI 的知识普及。
