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EEGLAB extensions and plugins

EEGLAB extensions or plugins allow users to build and publish new data processing and/or visualization functions using EEGLAB data structures and conventions. Plug-in functions can be easily used and tested by selecting the new menu items they introduce into the EEGLAB menus. EEGLAB can download and install Eplugins directly from the File → Manage EEGLAB extensions menu item.

Table of contents

Lists of plugins for different EEGLAB versions

The way plugins are handled has changed through EEGLAB history, leading to more automation in more recent versions and different systems for storing and managing plugins (the plugins themselves are often the same across the different plugin management systems). The list of plugins provided below is the same as the list of plugins available through the EEGLAB plugin manager of the corresponding EEGLAB version.

To install or update a plugin

Plug-ins may be installed using the EEGLAB plugin manager, using menu item File → Manage EEGLAB extensions.

Although no longer recommended, plugins can still be installed manually. After downloading the zip file for a plugin, uncompress the downloaded plugin file in the main EEGLAB “plugins” sub-directory. Remove the old version of the plugin if it is present in the directory. Then restart EEGLAB. During start-up, EEGLAB should print the following on the MATLAB command line:

> eeglab: adding plugin "eegplugin_myplugin" % (see >> help eegplugin_myplugin)

The plugin will typically have added one or more new items to the EEGLAB menus (often under the Import data or Tools headings).

To uninstall a plugin

Plug-ins can just as easily be removed from the EEGLAB extension manager. Alternatively, you may move or remove its folder from the EEGLAB plugins folder and restart EEGLAB.

To contribute a new plugin

See the simple instructions under How to contribute to EEGLAB to create EEGLAB compatible code.

Then, you may add your extension to the list above so that EEGLAB users can download it automatically from within EEGLAB. To do this, use this form. If you want to upload a new version of your plugin, you can use this simplified form.

Administrators, these are the maintenance pages to accept Pending plugin requests and Edit plugin information.

To access old versions of a plugin / extension

In case you need them, old versions of plugins are available for direct download at These cannot be installed through the EEGLAB extension manager. Simply download the zip file and uncompress it in the eeglab/plugins/ folder (and make sure you remove any other version of the plugin you might have installed).

We list below popular plugins available in EEGLAB. We have not assessed the methods they make available, so we recommend that EEG researchers thoroughly study and consider the basis of any methods they apply to experimental data. The list below is not complete, as there are currently 120 plugins available in EEGLAB. If your plugin has reached 500 downloads and it is not in the list below, please let us know.

EEGLAB default plugins

These plugins are distributed along with the EEGLAB code.

  • FIRfilt: Apply a variety of linear filters to EEGLAB data.

  • CleanRawData: Cleans raw EEG data using a variety of method, including Artifact Subspace Reconstruction.

  • DIPFIT: Dipole modeling of independent data components using a spherical or boundary element head model. Uses functions from the FIELDTRIP toolbox.

  • ICLabel: An automated EEG independent component classifier plugin for EEGLAB.

Data collection

Data import

These extensions allow importing various types of data. Although EEGLAB contains native functions to import some data formats, the plugins below support other formats. There are many data import extension plugins. We only include the most popular ones below.

  • bids-matlab-tools: The bids-matlab-tool repository contains a collection of functions to import and export BIDS (Brain Imaging Data Structure)-formated experiments.

  • BIOSIG: Import/export data in a wide variety of data formats.

  • FileIO: Toolbox allowing data import in multiple data formats. It contains functions redundant with EEGLAB but also contains unique functions.

  • ANTeepimport: Import data files in the EEP format of the ANT EEG company.

  • bva-io: Import/export files from/to the Brain Vision Software Analyser suite.

  • neuroscanio: Import/export files from/to the Neuroscan software.

  • MFFMATLABIO: Import/export files from/to the EGI company in MFF format.

  • xdfimport: Import files in XDF (LSL) format (EEG stream and EEG marker stream only).

  • Mobilab: Import files in XDF (LSL) format and allow fusing streams at different sampling rates for joint processing in EEGLAB.


  • IIRfilt: Apply short non-linear infinite impulse response filters to EEGLAB data.

  • REST: A method to standardize a reference of scalp EEG recordings to a point at infinity.

  • AAR: The Automatic Artifact Removal toolbox aims to integrate several state-of-the-art methods for the automatic removal of ocular and muscular artifacts in the electroencephalogram (EEG).

  • VisEd: The Vised Marks extension for EEGLAB adds editing functions to the native eegplot data scrolling figure. Specifically, it allows adding/editing event markers, flagging channels/components, flagging time periods, and displaying the properties of the marks structure.

  • get_chanlocs: The get_chanlocs EEGLAB plugin locates 3-D electrode positions from a 3-D scanned head image. A tutorial on how to acquire these images with off-the-shelf equipment is included.

EEG/fMRI artifact removal

  • FMRIB: Remove fMRI-environment artifacts from EEGLAB data. This extension allows the removal of scanner-related artifacts from EEG data collected during fMRI scanning. See also the GitHub repository.

  • BERGEN: Removal of fMRI-related gradient artifacts from simultaneous EEG-fMRI data. The BERGEN extension for EEGLAB provides a GUI with different methods for gradient artifact correction.

ICA-based artifact rejection and component classification

  • MARA: Automatic identification of artifactual independent components. MARA is a linear classifier that learns from expert ratings by extracting six features from the spatial, spectral, and temporal domains.

  • FASTER: implements a fully automated, unsupervised method for processing of high-density EEG data. FASTER includes common features such as data importing, epoching, re-referencing, grand average creation, automated channel, epoch, and artifact rejection based on ICA.

  • ADJUST: A completely automatic algorithm that identifies artifact-related Independent Components by combining stereotyped artifact-specific spatial and temporal features.

  • CORRMAP: Semi-automatic identification of common EEG artifacts based on a template. The CORRMAP extension consists of a set of MATLAB functions allowing the identification and clustering of independent components representing common EEG artifacts.

  • CIAC: The cochlear implant artifact correction is a semi-automatic ICA-based tool for the correction of electrical artifacts originating from cochlear implants.

  • RELICA: The goal of RELICA is to identify IC processes that are most stably separated from the decomposition data across many random bootstrap selections of its data frames or epochs.

  • MP_clustering: A toolbox for Measure Projection Analysis for projecting EEG measures tagged by source location into a common template brain space, testing local spatial measure consistency, and parsing measure-consistent brain areas into measure-separable domains.

  • REGICA: An extension to remove EOG artifacts by regression performed on ICA components. A semi-simulated dataset that might be used in any artifact rejection study is also available.

  • SASICA: SASICA is an EEGLAB plugin to help you reject/select independent components based on various properties of these components.

  • Automagic: Automagic is a MATLAB-based toolbox for preprocessing of EEG-datasets. It has been developed with the intention to offer a user-friendly pre-processing software for big (and small) EEG datasets.

  • AMICA: Adaptive Mixture Independent Component Analysis (AMICA) is a binary program and EEGLAB plugin that performs an independent component analysis (ICA) decomposition on input data, potentially with multiple ICA models. Also, consider downloading the postAmicaUtility plugin.

Statistics and signal processing

  • Fieldtrip-lite: Fieldtrip is a stand-alone toolbox but may also act as an EEGLAB extension providing source localization methods and additional statistical methods.

  • LIMO: The LInear MOdelling of MEEG data (LIMO MEEG) toolbox is an EEGLAB plugin dedicated to the statistical analysis of MEEG data.

  • ERPLAB: The ERPLAB Toolbox is a set of open-source MATLAB routines for analyzing ERP data that operate as a set of extensions to EEGLAB.

  • EYE-EEG: The EYE-EEG Toolbox is an extension of EEGLAB developed with the goal of facilitating integrated analyses of electrophysiological and oculomotor data.

  • mass_univ: The Mass Univariate ERP Toolbox is a freely available set of MATLAB functions for performing mass univariate analyses of event-related brain potentials (ERPs), a noninvasive measure of neural activity popular in cognitive neuroscience.

  • bioelectromag: The bioelectromagnetism MATLAB toolbox is interfaced in this extension to plot average ERPs and to find their minima and maxima (peak finding). Only a few files from this toolbox are included in this extension.

Source and connectivity analysis

  • SIFT: The Source Information Flow Toolbox and EEGLAB plugin computes a wide variety of multivariate effective causal models of source-resolved EEG data. Interactive visualizations and animations of event-related ‘information flow’ networks are included.

  • NFT: The Neuroelectromagnetic Forward Head Modeling Toolbox builds custom Boundary Element Method (BEM) and Finite Element Model (FEM) forward head models from subject MR head images and/or from an MNI template brain model warps to measured electrode positions.

  • PACTools: The Event-Related PACTools is an EEGLAB plugin to compute phase-amplitude coupling in single-subject data. In addition to traditional methods to compute PAC, the plugin includes the Instantaneous and Event-Related implementation of the Mutual Information Phase-Amplitude Coupling Method (MIPAC).

  • PACT: PACT is an EEGLAB extension for computing cross-frequency phase-amplitude coupling.

  • erpsource: Source localization of ERPs using eLoreta.

High performance computing

  • nsgportal: The NSG EEGLAB portal to High-Performance Computing may be used to run EEGLAB scripts on high-performance computing resources via the freely available Neuroscience Gateway Portal (NSG) to the NSF-sponsored Expanse supercomputer of the San Diego Supercomputer Center.