Federal University of Uberlândia, Uberlândia, Brazil. Lyon Neuroscience Research Center, Lyon, France.
J Neural Eng. 2020 Jan 24;17(1):016035. doi: 10.1088/1741-2552/ab581d.
Brain-machine interfaces (BMIs) use brain signals to control closed-loop systems in real-time. This comes with substantial challenges, such as having to remove artifacts in order to extract reliable features, especially when using electroencephalography (EEG). Some approaches have been described in the literature to address online artifact correction. However, none are being used as a 'gold-standard' method, and no research has been conducted to analyze and compare their respective effects on statistical data analysis (inference-based decision).
In this paper, we evaluate methods for artifact correction and describe the necessary adjustments to implement them for online EEG data analysis.
We investigate the following methods: artifact subspace reconstruction (ASR), fully online and automated artifact removal for brain-computer interfacing (FORCe), online empirical model decomposition (EMD), and online independent component analysis. For assessment, we simulated online data processing using real data from an auditory oddball task. We compared the above methods with classical offline data processing, in their ability (i) to reveal a significant mismatch negativity (MMN) response to auditory stimuli; (ii) to reveal the more subtle modulation of the MMN by contextual changes (namely, the predictability of the sound sequence), and (iii) to identify the most likely learning process that explains the MMN response.
Our results show that ASR and EMD are both able to reveal a significant MMN and its modulation by predictability, and even appear more sensitive than the offline analysis when comparing alternative models of perception underlying auditory evoked responses.
ASR and EMD show many advantages when compared to other online artifact correction methods. Besides, subtle modulation analysis of the MMN, embedded in perception computational models is a novel method for assessing the quality of artifact correction methods.
脑机接口（BMI）使用脑信号实时控制闭环系统。这带来了很大的挑战，例如必须去除伪影以提取可靠的特征，尤其是在使用脑电图（EEG）时。文献中已经描述了一些解决在线伪影校正的方法。然而，没有一种方法被用作“黄金标准”方法，也没有研究分析和比较它们各自对统计数据分析（基于推理的决策）的影响。
在本文中，我们评估了伪影校正方法，并描述了为在线 EEG 数据分析实施这些方法所需的调整。
我们研究了以下方法：伪影子空间重建（ASR）、用于脑机接口的完全在线和自动伪影去除（FORCE）、在线经验模型分解（EMD）和在线独立成分分析。为了评估，我们使用来自听觉Oddball 任务的真实数据模拟了在线数据处理。我们将上述方法与经典的离线数据处理进行了比较，以评估它们在以下方面的能力：（i）揭示对听觉刺激的显著失匹配负波（MMN）反应；（ii）揭示 MMN 受上下文变化（即声音序列的可预测性）的微妙调制；（iii）识别最有可能解释 MMN 反应的学习过程。
我们的结果表明，ASR 和 EMD 都能够揭示显著的 MMN 及其可预测性调制，并且在比较听觉诱发反应背后的替代感知模型时，甚至比离线分析更敏感。
ASR 和 EMD 与其他在线伪影校正方法相比具有许多优势。此外，嵌入感知计算模型中的 MMN 的微妙调制分析是评估伪影校正方法质量的新方法。
