Pion-Tonachini Luca, Kreutz-Delgado Ken, Makeig Scott
Swartz Center for Computational Neuroscience, University of California San Diego, 9500 Gilman Drive, La Jolla CA 92093, USA.
Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla CA 92093, USA.
Data Brief. 2019 Jun 8;25:104101. doi: 10.1016/j.dib.2019.104101. eCollection 2019 Aug.
The ICLabel dataset is comprised of training and test sets of a set of spatiotemporal features of electroencephalographic (EEG) independent components (IC). The feature sets were computed for over 200,000 EEG ICs from more than 6,000 existing EEG recordings. More than 8,000 of these ICs have accompanying crowdsourced IC labels across seven IC categories: and . The feature-sets included in the ICLabel dataset are scalp topography images, channel-based scalp topography measures, power spectral densities (PSD) measures (median, variance and kurtosis) and autocorrelation functions, equivalent current dipole (ECD) model fits for single and bilaterally symmetric dipole models, plus features used in several published IC classifier approaches. The is comprised of 130 ICs from 10 datasets not included in the training set. Each of the test set ICs has an associated IC label estimated based on labels provided by six ICA-EEG experts. Files necessary for adding to and amending the dataset are also included, plus a python class containing useful methods for interacting with the dataset, and IC classifications produced by several existing IC classifiers. These data are linked to the article, "ICLabel: An automated electroencephalographic independent component classifier, dataset, and website" [1]. An active tutorial and crowdsourcing website is available: i
ICLabel数据集由脑电图（EEG）独立成分（IC）的一组时空特征的训练集和测试集组成。这些特征集是针对来自6000多个现有EEG记录的20多万个EEG IC计算得出的。其中8000多个IC在七个IC类别中有相应的众包IC标签： 和 。ICLabel数据集中包含的特征集有头皮地形图图像、基于通道的头皮地形图测量、功率谱密度（PSD）测量（中位数、方差和峰度）以及自相关函数、单偶极子模型和双侧对称偶极子模型的等效电流偶极子（ECD）模型拟合，以及几种已发表的IC分类器方法中使用的特征。测试集由来自训练集中未包含的10个数据集的130个IC组成。每个测试集IC都有一个基于六位ICA-EEG专家提供的标签估计的相关IC标签。还包括添加和修改数据集所需的文件，以及一个包含与数据集交互的有用方法的Python类，还有几个现有IC分类器产生的IC分类。这些数据与文章《ICLabel：一个自动脑电图独立成分分类器、数据集和网站》[1]相关联。有一个活跃的教程和众包网站：i
