# EEGLab: Software Analysis of Human Brain Dynamics

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $629,552

## 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 used for recording in
lifelike situations. A major shift in scientific perspective on the nature and use of human electrophysiological data is now
ongoing, a shift to using EEG data as a source-resolved, relatively high-resolution 3D cortical source imaging modality.
The EEGLAB signal processing environment is a readily extensible open-source software project of the Swartz Center for
Computational Neuroscience (SCCN) of the University of California, San Diego (UCSD). EEGLAB 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 21 years later, its reference paper {Delorme, 2004 #1} has over
18,450 citations (increasing by 6.8 per day), its opt-in EEGLAB discussion email list links over 6,000 researchers, its
EEGLAB news reaches over 15,000, 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. EEGLAB
citations and other metrics show that EEGLAB adoption is still growing steadily. Here, we will greatly augment the power
of the EEGLAB environment by providing support for processing both intracranial (iEEG, sEEG) and mobile brain/body
imaging (MoBI) data (EEG and behavior), and will further integrate tools for performing high-resolution source imaging
from EEG (or iEEG) data. Its suitability for multi-modal brain/behavioral recording is one of the strengths of EEG
recording compared to other imaging modalities. Multimodal data review and processing tools will be incorporated into
EEGLAB, to further support the development of tools for processing mobile brain imaging data. We will develop a
framework for source connectivity analysis using (1) a hierarchical Bayesian framework for clustering effective source
processes identified by independent component analysis on multiple measures across subjects and studies and (2) region
of interest (ROI) dynamics estimation by beamforming. We will further revise the EEGLAB architecture to use a file and
metadata organization compatible with the Brain Imaging Data Structure (BIDS) specifications. These tools will integrate
the Hierarchical Event Descriptor (HED) event annotation system to enable innovative meta-analyses across data from
multiple studies. These continuing developments will further the use of non-invasive and (as per clinical need) invasive
human electrophysiology for 3-D functional cortical brain imaging, thereby accelerating progress in noninvasive basic and
clinical human brain research using highly time- and space-resolved measures of brain electrophysiological dynamics.

## Key facts

- **NIH application ID:** 10886795
- **Project number:** 5R01NS047293-20
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Arnaud Delorme
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $629,552
- **Award type:** 5
- **Project period:** 2004-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10886795

## Citation

> US National Institutes of Health, RePORTER application 10886795, EEGLab: Software Analysis of Human Brain Dynamics (5R01NS047293-20). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10886795. Licensed CC0.

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