# EEGLAB: Software for Analysis of Human Brain Dynamics

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $578,082

## Abstract

Electroencephalography (EEG), the first function brain activity imaging modality, has several natural advantages over
metabolic brain imaging modalities. EEG is noninvasive, low cost, and lightweight enough to be highly mobile. Two
major shifts in scientific perspective on the nature and use of human electrophysiological data are now ongoing. The first
is a shift to using EEG data as a source-resolved, relatively high-resolution cortical source imaging modality. The
EEGLAB signal processing environment, an open source software project of the Swartz Center for Computational
Neuroscience (SCCN) of the University of California, San Diego (UCSD), began as a set of EEG data analysis running on
Matlab (The Mathworks, Inc.) released by Makeig on the World Wide Web in 1997. EEGLAB was first released from
SCCN in 2001. Now nearly twenty years later, the EEGLAB reference paper [4] has over 6,750 citations (now increasing
by over 4 per day), the opt-in EEGLAB discussion email list links 6,000 researchers, the EEGLAB news list over 15,000
researchers, and an independent 2011 survey of 687 research respondents reported EEGLAB to be the software
environment most widely used for electrophysiological data analysis in cognitive neuroscience. Our statistics show that
after over the past four years, EEGLAB adoption is still growing steadily. Here, we will develop a framework for
thorough comparison of preprocessing methods, and will apply machine learning methods on the large body of data
collected by our laboratory to build optimized, automated data processing pipelines. We will greatly augment the power of
the EEGLAB environment by providing a cross-study meta-analysis capability and will revise the software architecture to
use a file and metadata organization compatible with the Brain Imaging Data Structure (BIDS) framework first developed
for fMRI/MRI data archiving. These tools will integrate the HED annotating system allowing for meta-analysis across
large corpus of studies. We will implement beamforming within EEGLAB. We will develop a hierarchical Bayesian
framework for clustering effective sources on multiple measures across subjects and studies, and will develop tools to
perform statistical testing on information flow measures at these scales. Although EEG and MEG recording have co-
existed for four decades, little available software can combine both data types, recorded concurrently (`MEEG' data), to
enhance source separation. We recently showed that ICA decomposition also allows joint MEEG effective source
decomposition and will integrate MEG and joint MEEG data decomposition and imaging into the EEGLAB tool set. We
will build tools to use MRI- and fMRI-derived anatomical atlases to inform the interpretation of EEG and MEG brain
source dynamics. These radical improvements will further the use of non-invasive human electrophysiology for 3-D
functional cortical brain imaging in the U.S. and worldwide, thereby accelerating progress in noninvasive basic and...

## Key facts

- **NIH application ID:** 10200896
- **Project number:** 5R01NS047293-17
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Arnaud Delorme
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $578,082
- **Award type:** 5
- **Project period:** 2004-07-15 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200896

## Citation

> US National Institutes of Health, RePORTER application 10200896, EEGLAB: Software for Analysis of Human Brain Dynamics (5R01NS047293-17). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10200896. Licensed CC0.

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