Neonatal EEG Analysis Toolbox (NEAT)

About NEAT

Many qualitative and quantitative metrics have been developed to analyze the output of limited-channel EEG recordings in neonates. A significant roadblock in the widespread dissemination of these techniques is the device-specific, “black-box” nature of the analytic algorithms, preventing comparison between studies and the obsolescence of old recordings as software is abandoned. We have developed NEAT (Neonatal EEG Analysis Toolbox) as an open-source software platform to overcome these challenges. NEAT runs within MATLAB and is designed to work with any single-channel EEG input, regardless of source or sampling rate.


Figure 1. Example of aEEG output generated by NEAT from a single-channel EEG

Figure 2. Longitudinal SEF90as calculated by NEAT

Figure 3. Experimental NEAT GUI

Current Version Features (r1 - 06/21/2017)

  • Includes two command-line MATLAB functions to generate aEEG signal and calculate spectral edge frequency
  • Includes experimental GUI, allowing for linked navigation of raw/aEEG signals
  • Includes brm_convert, a GUI for conversion of BRM files captured using the BrainZ BRM monitor to *.MAT files


  • The core algorithms and clinical validation was presented at the Pediatric Academic Societies Annual Meeting in San Francisco, CA - Moscone West Conference Center, May 8, 2017, 4:15p-7:30p. Session 3849.2, Board 514
  • Manuscript forthcoming, details will be posted once available


Please visit our code and sample files hosted on Mendeley Data.

Note: This download link replaces the previously hosted version.

System Requirements

  • MATLAB r2016a or later
  • Signal Processing toolbox

Legal Notice

Neonatal EEG Analysis Toolbox (NEAT) for the MATLAB Scientific Programming Language
Copyright (c) 2017 Washington University
Created by: Zachary Vesoulis, Paul Gamble, Sid Jain, Amit Mathur

Washington University hereby grants to you a non-transferable, non-exclusive, royalty-free, non-commercial, research license to use and copy the computer code that may be downloaded within this site (the “Software”). You agree to include this license and the above copyright notice in all copies of the Software. The Software may not be distributed, shared, or transferred to any third party. This license does not grant any rights or licenses to any other patents, copyrights, or other forms of intellectual property owned or controlled by Washington University. If interested in obtaining a commercial license, please contact Washington University's Office of Technology Management (

© 2020 by Washington University in St. Louis
One Brookings Drive, St. Louis, MO 63130