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.

中文翻译：

---

基于稳态视觉诱发电位的脑机接口的自适应时间段分析。

近年来，对非侵入性脑机接口（BCI）的研究已为潜在的最终用户带来了重大改进。但是，用户体验和BCI文盲问题仍然是挑战性领域，需要解决才能获得强大而灵活的临床应用。在这项研究中，我们解决了用于稳态视觉诱发电位（SSVEP）检测的时间段选择。与基于SSVEP的BCI相比，此问题已被广泛解决，用于检测与事件相关的电位。该参数的选择通常是固定的，并且直接影响检测精度和信息传输速率。我们建议将时间段的问题转移到阈值的选择上，以确定是否已正确检测到响应。我们考虑了两个开放数据集来对方法的原理进行基准测试。结果支持这样的结论：与用户选择的时间段（79.56位/分钟）相比，基于阈值的选择，针对每个试验的自适应时间段可以导致更高的ITR（86.92位/分钟）。 ）或组级别（73.78位/分钟）。最后，结果表明可以根据类别数自动确定阈值。这样的方法可以利用基于SSVEP的BCI的素养。结果表明可以根据类别数自动确定阈值。这样的方法可以利用基于SSVEP的BCI的素养。结果表明可以根据类别数自动确定阈值。这样的方法可以利用基于SSVEP的BCI的素养。