University of Coimbra, Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290, Coimbra, Portugal.
Epilepsy Center, Medical Center - University of Freiburg, Department Neurosurgery, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany.
Sci Data. 2022 Aug 20;9(1):512. doi: 10.1038/s41597-022-01524-x.
Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain's electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component analysis is one of the most used artifact removal methods. Independent component analysis separates data into different components, although it can not automatically reject the noisy ones. Therefore, experts are needed to decide which components must be removed before reconstructing the data. To automate this method, researchers have developed classifiers to identify noisy components. However, to build these classifiers, they need annotated data. Manually classifying independent components is a time-consuming task. Furthermore, few labelled data are publicly available. This paper presents a source of annotated electroencephalogram independent components acquired from patients with epilepsy (EPIC Dataset). This dataset contains 77,426 independent components obtained from approximately 613 hours of electroencephalogram, visually inspected by two experts, which was already successfully utilised to develop independent component classifiers.
头皮脑电图是一种非侵入性的多通道生物信号，记录大脑的电活动。它非常容易受到噪声的干扰，这些噪声可能会掩盖重要的数据。独立成分分析是最常用的去除伪影的方法之一。独立成分分析将数据分为不同的成分，尽管它不能自动拒绝有噪声的成分。因此，需要专家来决定在重建数据之前必须去除哪些成分。为了实现这种方法的自动化，研究人员开发了分类器来识别有噪声的成分。然而，要构建这些分类器，他们需要有注释数据。手动分类独立成分是一项耗时的任务。此外，可用的标记数据很少。本文提供了一个从癫痫患者（EPIC 数据集）获得的有注释的脑电图独立成分的来源。该数据集包含 77426 个独立成分，是从大约 613 小时的脑电图中获取的，由两位专家进行了视觉检查，这些独立成分已经成功地用于开发独立成分分类器。
