
TechRxiv
A preprint server for health sciences.

The global standard for open-source electrophysiological signal processing and ICA decomposition.

EEGLAB is an interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data. Developed by the Swartz Center for Computational Neuroscience (SCCN) at UCSD, it represents the architectural backbone of modern computational neuroscience. In 2026, EEGLAB remains the dominant platform due to its robust implementation of Independent Component Analysis (ICA), allowing researchers to isolate brain signals from artifacts with high precision. Its architecture is built around a multi-layered plugin system, enabling the integration of hundreds of community-developed toolboxes for specialized tasks like source localization, connectivity analysis, and deep learning classification. The platform has evolved to fully support BIDS (Brain Imaging Data Structure) for standardized data sharing and high-performance computing (HPC) clusters via its sister project, NSG (Neuroscience Gateway). While historically MATLAB-dependent, the 2026 ecosystem offers a compiled standalone version and growing Python interoperability, maintaining its status as a critical utility for clinical research, cognitive science, and the development of next-generation brain-computer interfaces.
EEGLAB is an interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data.
Explore all tools that specialize in ica decomposition. This domain focus ensures EEGLAB delivers optimized results for this specific requirement.
Advanced blind source separation to isolate neural activity from eye blinks, muscle noise, and line noise.
An automated electroencephalographic independent component classifier trained on thousands of components.
Full compliance with the Brain Imaging Data Structure for standardized metadata and file naming.
Models dynamical interactions between different brain regions using vector autoregressive models.
Creates realistic head models from MRIs for accurate source localization.
Built-in extension architecture for one-click installation of community tools.
Data structure designed for group-level statistics and longitudinal analysis.
Install MATLAB (R2023b or later recommended).
Download the latest EEGLAB distribution from SCCN or GitHub.
Add the EEGLAB folder to your MATLAB path.
Type 'eeglab' in the command window to initialize the GUI and variables.
Use 'File > Import Data' to load raw electrophysiological files.
Define channel locations using a standard template or custom coordinate file.
High-pass filter data (usually 1Hz) to prepare for ICA.
Run 'Run ICA' using the Infomax or AMICA algorithm for component decomposition.
Utilize the 'ICLabel' plugin to automatically classify brain and non-brain components.
Export processed data or save as a '.set' file for group-level analysis.
All Set
Ready to go
Verified feedback from other users.
"Universally regarded as the most flexible and scientifically rigorous EEG analysis platform."
Post questions, share tips, and help other users.

A preprint server for health sciences.

Connect your AI agents to the web with real-time search, extraction, and web crawling through a single, secure API.

A large conversational telephone speech corpus for speech recognition and speaker identification research.

STRING is a database of known and predicted protein-protein interactions.

A free and open-source software package for the analysis of brain imaging data sequences.

Complete statistical software for data science with powerful statistics, visualization, data manipulation, and automated reporting in one intuitive platform.