文章基本信息

标题：EPIC: Annotated epileptic EEG independent components for artifact reduction
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作者：Fábio Lopes ; Adriana Leal ; Júlio Medeiros 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-9
DOI：10.1038/s41597-022-01524-x
语种：English
出版社：Nature Publishing Group
摘要：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 diferent 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 classifers to identify noisy components . However, to build these classifers, 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 classifers .