Related Experiment Video
Updated: Dec 24, 2025
PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
Published on: June 6, 2025
The Maryland analysis of developmental EEG (MADE) pipeline
Ranjan Debnath1, George A Buzzell1,2, Santiago Morales1,2
1Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA.
Insights
Pediatric EEG data require specialized preprocessing due to artifacts. The Maryland Analysis of Developmental EEG (MADE) pipeline offers an automated solution for cleaner pediatric electroencephalogram (EEG) data analysis.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Pediatric EEG signals present unique challenges, including shorter recordings and higher artifact levels compared to adults.
- Existing automated preprocessing pipelines often lack suitability for the specific demands of pediatric EEG data.
- Standardization in pediatric EEG preprocessing is crucial for reliable research outcomes.
Purpose of the Study:
- To develop an automated and standardized preprocessing pipeline for pediatric EEG data.
- To address the scarcity of robust preprocessing tools for developmental electroencephalogram (EEG) analysis.
- To ensure minimal data loss during artifact removal in pediatric EEG.
Main Methods:
- Development of the Maryland Analysis of Developmental EEG (MADE) pipeline, an automated system using EEGLAB and custom scripts.
- Compatibility with diverse hardware, populations, artifact levels, and recording durations.
- Processing of both event-related and resting-state EEG data from raw files.
Main Results:
- The MADE pipeline successfully processes raw pediatric EEG data into clean, analyzable formats.
- The pipeline generates a detailed report to assess the quality of the processed data.
- Customized features within MADE are particularly beneficial for pediatric EEG data.
Conclusions:
- The MADE pipeline provides a standardized and automated approach to pediatric EEG preprocessing.
- It facilitates more reliable analysis of developmental EEG data by mitigating artifacts and noise.
- The freely available MADE pipeline supports broader adoption and advancement in pediatric neuroscience research.
Abstract:
Compared to adult EEG, EEG signals recorded from pediatric populations have shorter recording periods and contain more artifact contamination. Therefore, pediatric EEG data necessitate specific preprocessing approaches in order to remove environmental noise and physiological artifacts without losing large amounts of data. However, there is presently a scarcity of standard automated preprocessing pipelines suitable for pediatric EEG. In an effort to achieve greater standardization of EEG preprocessing, and in particular, for the analysis of pediatric data, we developed the Maryland analysis of developmental EEG (MADE) pipeline as an automated preprocessing pipeline compatible with EEG data recorded with different hardware systems, different populations, levels of artifact contamination, and length of recordings. MADE uses EEGLAB and functions from some EEGLAB plugins and includes additional customized features particularly useful for EEG data collected from pediatric populations. MADE processes event-related and resting state EEG from raw data files through a series of preprocessing steps and outputs processed clean data ready to be analyzed in time, frequency, or time-frequency domain. MADE provides a report file at the end of the preprocessing that describes a variety of features of the processed data to facilitate the assessment of the quality of processed data. In this article, we discuss some practical issues, which are specifically relevant to pediatric EEG preprocessing. We also provide custom-written scripts to address these practical issues. MADE is freely available under the terms of the GNU General Public License at https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline.
