Faculty of Education, The University of Hong Kong, Hong Kong, People's Republic of China.
Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, United States of America.
J Neural Eng. 2022 Feb 2;19(1). doi: 10.1088/1741-2552/ac42b6.
Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research.We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio.SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI.The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.
神经适应性范式可以系统地评估跨许多不同实验参数的事件相关电位 (ERP) 特征，这有可能提高 ERP 研究结果的普遍性，并通过确定 ERP 对特定临床人群差异最大的确切实验条件来帮助加速基于 ERP 的生物标志物发现。在线获得稳健可靠的 ERP 是基于 ERP 的神经适应性研究的前提。涉及的关键步骤之一是正确实时隔离脑电图伪迹，因为它们会导致大量的方差，如果不加以去除，将会极大地扭曲获得的 ERP。另一个值得关注的关键因素是在线处理伪迹方法的计算成本。这项工作旨在开发和验证一种具有成本效益的系统，以支持基于 ERP 的神经适应性研究。我们开发了一种简单的在线伪迹处理方法，基于单试 PCA 的伪迹去除 (SPA)，基于方差分布二分法来区分伪迹和神经活动。然后，我们将该方法应用于基于 ERP 的神经适应性范式中，其中贝叶斯优化用于搜索单独最佳的刺激间间隔 (ISI)，以生成具有最高信噪比的 ERP。SPA 与其他离线和在线算法进行了比较。结果表明，SPA 在计算效率和 ERP 模式的保留方面都表现出了良好的性能。基于 SPA，贝叶斯优化过程能够快速找到单独最佳的 ISI。目前的工作提出了一种简单但非常具有成本效益的方法，该方法已经在提取 ERP、保留 ERP 效应和更好地支持基于 ERP 的神经适应性范式方面得到了验证。
