The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low-cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. Independent components (IC) found by ICA decomposition can be manually inspected, selected, and interpreted, but doing so requires both time and practice as ICs have no order or intrinsic interpretations and therefore require further study of their properties. Alternatively, sufficiently-accurate automated IC classifiers can be used to classify ICs into broad source categories, speeding the analysis of EEG studies with many subjects and enabling the use of ICA decomposition in near-real-time applications. While many such classifiers have been proposed recently, this work presents the ICLabel project comprised of (1) the ICLabel dataset containing spatiotemporal measures for over 200,000 ICs from more than 6000 EEG recordings and matching component labels for over 6000 of those ICs, all using common average reference, (2) the ICLabel website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier, freely available for MATLAB. The ICLabel classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The classifier outperforms or performs comparably to the previous best publicly available automated IC component classification method for all measured IC categories while computing those labels ten times faster than that classifier as shown by a systematic comparison against other publicly available EEG IC classifiers.

The ICLabel dataset is comprised of training and test sets of a set of spatiotemporal features of electroencephalographic (EEG) independent components (IC). The ICLabel training set 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: Brain, Muscle, Eye, Heart, Line Nosie, Channel Noise, and Other. 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 ICLabel test set 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: iclabel.ucsd.edu/tutorial/overview.

The ICLabel dataset is comprised of training and test sets of a set of spatiotemporal features of electroencephalographic (EEG) independent components (IC). The ICLabel training set 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: Brain, Muscle, Eye, Heart, Line Nosie, Channel Noise, and Other. 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 ICLabel test set 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: iclabel.ucsd.edu/tutorial/overview.

There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formation and dissolution of active cortical sources and brain networks. However, unsupervised approaches to identify and model these changes in brain dynamics as continuous transitions between quasi-stable brain states using unlabeled, noninvasive recordings of brain activity have been limited. This study explores the use of adaptive mixture independent component analysis (AMICA) to model multichannel electroencephalographic (EEG) data with a set of ICA models, each of which decomposes an adaptively learned portion of the data into statistically independent sources. We first show that AMICA can segment simulated quasi-stationary EEG data and accurately identify ground-truth sources and source model transitions. Next, we demonstrate that AMICA decomposition, applied to 6-13 channel scalp recordings from the CAP Sleep Database, can characterize sleep stage dynamics, allowing 75% accuracy in identifying transitions between six sleep stages without use of EEG power spectra. Finally, applied to 30-channel data from subjects in a driving simulator, AMICA identifies models that account for EEG during faster and slower response to driving challenges, respectively. We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. AMICA thus provides a generic unsupervised approach to identifying and modeling changes in EEG dynamics. Applied to continuous, unlabeled multichannel data, AMICA may likely be used to detect and study any changes in cognitive states.