# ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

arXiv: https://arxiv.org/abs/1901.07915 · HTML: https://mlsanity.com/paper/1901.07915/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2019.

## Built on by (most cited first, 30 of 46)

- [EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction](https://mlsanity.com/paper/2111.05100/) (2021)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2112.06654/) (2021)
- [IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal](https://mlsanity.com/paper/2111.10026/) (2021)
- [LEAD: An EEG Foundation Model for Alzheimer's Disease Detection](https://mlsanity.com/paper/2502.01678/) (2025)
- [Is Limited Participant Diversity Impeding EEG-based Machine Learning?](https://mlsanity.com/paper/2503.13497/) (2025)
- [Embedding Decomposition for Artifacts Removal in EEG Signals](https://mlsanity.com/paper/2112.00989/) (2021)
- [Reading Task Classification Using EEG and Eye-Tracking Data](https://mlsanity.com/paper/2112.06310/) (2021)
- [Artifact Detection and Correction in EEG data: A Review](https://mlsanity.com/paper/2106.13081/) (2021)
- [SPEED: Scalable Preprocessing of EEG Data for Self-Supervised Learning](https://mlsanity.com/paper/2408.08065/) (2024)
- [The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary study](https://mlsanity.com/paper/2505.13021/) (2025)
- [Neuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Control](https://mlsanity.com/paper/2603.25968/) (2026)
- [Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimation](https://mlsanity.com/paper/2302.12710/) (2023)
- [A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition](https://mlsanity.com/paper/2405.07260/) (2024)
- [EEG Artifact Detection and Correction with Deep Autoencoders](https://mlsanity.com/paper/2502.08686/) (2025)
- [CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction](https://mlsanity.com/paper/2210.05988/) (2022)
- [Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario](https://mlsanity.com/paper/2305.08044/) (2023)
- [A multi-artifact EEG denoising by frequency-based deep learning](https://mlsanity.com/paper/2310.17335/) (2023)
- [ADformer: A Multi-Granularity Spatial-Temporal Transformer for EEG-Based Alzheimer Detection](https://mlsanity.com/paper/2409.00032/) (2024)
- [Multi-class Decoding of Attended Speaker Direction Using Electroencephalogram and Audio Spatial Spectrum](https://mlsanity.com/paper/2411.06928/) (2024)
- [How Much Data is Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings](https://mlsanity.com/paper/2411.11886/) (2024)
- [TransformEEG: Towards Improving Model Generalizability in Deep Learning-based EEG Parkinson's Disease Detection](https://mlsanity.com/paper/2507.07622/) (2025)
- [Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2507.14698/) (2025)
- [CogEEGAgent: Toward Autonomous Cognitive EEG Analysis with Grounded Execution and Selection-Aware Verification](https://mlsanity.com/paper/2607.25045/) (2026)
- [Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition](https://mlsanity.com/paper/2209.05849/) (2022)
- [hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications](https://mlsanity.com/paper/2312.00799/) (2023)
- [Human Brain Exhibits Distinct Patterns When Listening to Fake Versus Real Audio: Preliminary Evidence](https://mlsanity.com/paper/2402.14982/) (2024)
- [Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy](https://mlsanity.com/paper/2407.08316/) (2024)
- [ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals](https://mlsanity.com/paper/2409.07326/) (2024)
- [Automatic EEG Independent Component Classification Using ICLabel in Python](https://mlsanity.com/paper/2411.17721/) (2024)

## Abstract (arXiv)

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 particular 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) an IC dataset containing spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG recordings, (2) a website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier. The classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The ICLabel classifier outperforms or performs comparably to the previous best publicly available method for all measured IC categories while computing those labels ten times faster than that classifier as shown in a rigorous comparison against all other publicly available EEG IC classifiers.