The ICLabel project presents an automated electroencephalographic (EEG) independent component (IC) classifier, dataset, and website. The ICLabel classifier improves upon existing methods by enhancing accuracy and computational efficiency. It is freely available for MATLAB and outperforms or performs comparably to previous best publicly available automated IC classification methods across all measured IC categories, while computing labels ten times faster. The ICLabel dataset contains spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG recordings, with matching component labels for over 6,000 of those ICs. The ICLabel website collects crowdsourced IC labels and educates EEG researchers about IC interpretation. The classifier uses a combination of scalp topographies, power spectral densities, and autocorrelation functions as input features. It is trained on a large dataset of EEG recordings and validated against other publicly available IC classifiers. The ICLabel classifier is compared to existing methods and shows superior performance in terms of accuracy, efficiency, and classification speed. The ICLabel project provides a comprehensive solution for automated IC classification, enabling faster and more accurate analysis of EEG data.The ICLabel project presents an automated electroencephalographic (EEG) independent component (IC) classifier, dataset, and website. The ICLabel classifier improves upon existing methods by enhancing accuracy and computational efficiency. It is freely available for MATLAB and outperforms or performs comparably to previous best publicly available automated IC classification methods across all measured IC categories, while computing labels ten times faster. The ICLabel dataset contains spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG recordings, with matching component labels for over 6,000 of those ICs. The ICLabel website collects crowdsourced IC labels and educates EEG researchers about IC interpretation. The classifier uses a combination of scalp topographies, power spectral densities, and autocorrelation functions as input features. It is trained on a large dataset of EEG recordings and validated against other publicly available IC classifiers. The ICLabel classifier is compared to existing methods and shows superior performance in terms of accuracy, efficiency, and classification speed. The ICLabel project provides a comprehensive solution for automated IC classification, enabling faster and more accurate analysis of EEG data.