IEEE J Biomed Health Inform. 2018 Sep;22(5):1350-1361. doi: 10.1109/JBHI.2017.2773999. Epub 2017 Nov 15.
Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments are becoming increasingly important in areas such as brain-computer interfaces and behavior science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm, which will be used as a preprocessing step in a driver monitoring application. The algorithm, named Automated aRTifacts handling in EEG (ARTE), is based on wavelets, independent component analysis, and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-min 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error, and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state-of-the-art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable, and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a preprocessing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.
自动分析在自然环境中获取的脑电图（EEG）信号在脑机接口和行为科学等领域变得越来越重要。然而，在这种环境中记录的 EEG 通常受到运动伪影和眼动的严重干扰。这对伪影处理提出了新的要求。本文的目的是提出一种自动脑电图伪影处理算法，该算法将作为驾驶员监控应用中的预处理步骤。该算法名为 EEG 中的自动 ARTifacts 处理（ARTE），基于小波、独立成分分析和层次聚类。该算法在一项包括 30 名驾驶员和 540 个 30 分钟 30 通道 EEG 记录的驾驶员困倦研究数据集上进行了测试。该算法由临床神经生理学家通过定量标准（信号质量指数、均方误差、相对误差和平均绝对误差）和通过证明其作为驾驶员监控预处理步骤的有用性进行评估，这里用驾驶员困倦分类进行了举例说明。所有结果均与一种称为 FORCe 的最先进算法进行了比较。定量和专家评估结果表明，这两种算法相当，并且这两种算法都显著降低了记录 EEG 信号中的伪影影响。当使用 ARTE 进行驾驶员困倦分类的预处理步骤时，分类精度提高了 5%，而使用 FORCe 时则提高了 2%。ARTE 的优势在于它是数据驱动的，不依赖于额外的参考信号或手动定义的阈值，因此非常适合用于动态环境，在这些环境中，通常会遇到不可预见和罕见的伪影。
