Xavier Fidêncio Aline, Klaes Christian, Iossifidis Ioannis
Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, Bochum, Germany.
Robotics and BCI Laboratory, Institute of Computer Science, Ruhr West University of Applied Sciences, Mülheim an der Ruhr, Germany.
Front Hum Neurosci. 2024 Jul 17;18:1390714. doi: 10.3389/fnhum.2024.1390714. eCollection 2024.
Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.
错误相关电位（ErrPs）是已知的作为对错误事件的反应而产生的脑信号。几项研究表明，不仅自我产生的错误，而且外部因素导致的错误也能引发这种事件相关电位。通过非侵入性技术可靠测量ErrPs的可能性增加了脑机接口（BCI）社区对使用此类信号来提高性能的兴趣，例如通过执行错误校正。通常需要进行广泛的校准会话，以收集足够的试验来训练特定于受试者的ErrP分类器。这个过程不仅耗时，而且让参与者感到厌烦。在本文中，我们探讨了ErrPs在闭环系统中的有效性，强调了它们对精确单试验分类的依赖性。为了保证我们使用的数据中存在ErrPs信号，并确保定义ErrPs的参数系统地变化，我们利用开源工具箱SEREEGA进行数据模拟。我们生成了训练实例，并在模拟和真实世界数据集上评估了通用分类器的性能，提出了一种有前途的替代传统校准技术的方法。结果表明，通用支持向量机分类器在每个验证数据集上的平衡准确率分别达到72.9%、62.7%、71.0%和70.8%。虽然在错误类别检测方面与留一受试者法表现相似，但所提出的分类器在无需进一步调整的情况下，在不同数据集和受试者之间显示出有前途的泛化能力。此外，通过使用SEREEGA，我们可以系统地调整参数以适应ErrP的变异性，促进闭环设置的系统验证。此外，我们的目标是开发一种通用的ErrP分类器，该分类器能够捕捉信号的变异性，使其能够确定真实脑电图数据中ErrP的存在或不存在。
