# The Open EEGLAB Portal Project

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $544,281

## 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 second is a shift from recording electrophysiological data with at best a
scant record of behavior (e.g., latencies of occasional button presses) to concurrently collecting and combining
EEG data with other data modalities (e.g., body motion capture, eye tracking, audio and video, ECG, EMG,
GSR, MEG, fMRI, etc.), paradigms that we term Mobile Brain/Body Imaging (MoBI) to capture brain activities
and subject actions during natural, motivated behavior.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 (Delorme & Makeig, 2004) has over 4,100 citations
(now increasing by over 3 per day), the opt-in EEGLAB discussion email list links over 5,500 researchers, the
EEGLAB news list over 15,400 researchers, and a survey of 687 research respondents reported EEGLAB to
be the software environment most widely used for electrophysiological data analysis in cognitive neuroscience.
Currently, at least 52 EEGLAB plug-in tool sets have been released by other researchers from many
laboratories. Here we propose, first, to greatly augment the power of the EEGLAB environment by enabling it
to perform time series, biophysical, and statistical analyses of multimodal as well as unimodal EEG data.
However, ever more precise analyses of large and multimodal data sets and studies require increasing
amounts of computational power, more than is readily available in many laboratories. Thus second, in
collaboration with the San Diego Supercomputer Center (SDSC) we propose to expand the current
Neuroscience Gatew​ ay (​nsgportal.org) services to enable EEGLAB users to freely run EEGLAB processing
scripts and pipelines on SDSC supercomputers. The proposed Open EEGLAB Portal will allow researchers to
submit any amount of unimodal or multimodal EEG data for parallel processing using standard or custom
EEGLAB processing pipelines. We will also develop and release first tools for meta-analysis of
source-resolved EEG measures ​across studies. Multimodal EEG analysis and source-level EEG analysis
accelerated by free use of supercomputing resources will give the EEG research community unprecedented
abilities to observe and model distributed cortical dynamics supporting h...

## Key facts

- **NIH application ID:** 9982308
- **Project number:** 5R01EB023297-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Arnaud Delorme
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $544,281
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9982308

## Citation

> US National Institutes of Health, RePORTER application 9982308, The Open EEGLAB Portal Project (5R01EB023297-04). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9982308. Licensed CC0.

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