P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant ($p<0.001$) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.

中文翻译：

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使用基于分类器的延迟估计来预测脑机接口精度的分类技术的比较

基于P300的脑机接口（BCI）性能容易受到延迟抖动的影响。为了研究延迟抖动对BCI系统性能的作用，我们提出了基于分类器的延迟估计（CBLE）方法。在我们之前的研究中，CBLE基于最小二乘（LS）和逐步线性判别分析（SWLDA）分类器。在这里，我们旨在扩展使用稀疏自动编码器（SAE）的CBLE方法，以将基于SAE的CBLE方法与基于LS和SWLDA的CBLE进行比较。新开发的基于SAE的CBLE和先前使用的方法也应用于新收集的数据集，以减少乱真相关的可能性。我们的结果表明，$p<0.001$）BCI准确性与估算的时延抖动之间呈负相关。此外，我们还检查了电极数量对每种分类技术的影响。我们的结果表明，总体而言，无论分类方法和电极数量如何，CBLE都能发挥作用。相反，电极数量对BCI性能的影响取决于分类器。