The Maryland Analysis of Developmental EEG (MADE) Pipeline

Abstract

<jats:title>Abstract</jats:title><jats:p>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.</jats:p><jats:p>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 customizable features particularly useful for EEG data collected from pediatric populations.</jats:p><jats:p>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 paper we discuss some practical issues, which are specifically relevant to pediatric EEG preprocessing. We also provide custom-written scripts to address these practical issues.</jats:p><jats:p>MADE is freely available under the terms of the GNU General Public License at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline">https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline</jats:ext-link>.</jats:p>

The influence of monetary reward on proactive and reactive control in adolescent males

“…All electrode impedances were below 50 kΩ prior to data collection. EEG analysis was conducted off-line using The Maryland Analysis of Developmental EEG (MADE) Pipeline ( Debnath et al, 2020 ), a standardized EEG pipeline based on MATLAB 2014b (MathWorks, Inc., Natick, MA) and the EEGLab Toolbox ( Delorme and Makeig, 2004 ). Data were high-pass filtered at 0.3 Hz and low-pass filtered at 49 Hz.…”

Behavioral Inhibition and Dual Mechanisms of Anxiety Risk: Disentangling Neural Correlates of Proactive and Reactive Control

“…During data collection, electrodes were referenced to electrode Cz. All pre-processing, including ocular artifact detection and removal, was performed with the Maryland Analysis of Developmental EEG (MADE) pipeline (Debnath et al, 2020), which utilizes MATLAB (The MathWorks, Natick, MA) functions from EEGLAB (Delorme & Makeig, 2004) and its plugins “FASTER” (Nolan et al, 2010), “ADJUST” (Mognon et al, 2011), and “ADJUSTED ADJUST” (Leach et al, 2020). Offline, data were re-referenced to an average reference and band-pass filtered from 0.3 to 50 Hz with a digital FIR filter.…”

Automatic classification of ICA components from infant EEG using MARA

“…Running head: Automated infant ICA classification 14 A fully automatic artifact rejection procedure was adopted, following procedures from commonly used toolboxes for EEG pre-processing in adults (Mullen 2012;Bigdely-Shamlo, et al, 2015) and infants (Gabard-Durham et al, 2018;Debnath et al, 2020). This was composed of the following steps: first, EEG data were high-pass filtered at 1Hz (FIR filter with a Hamming window applied: order 3381 and 0.25/ 25% transition slope, passband edge of 1hz and a cutoff frequency at -6db of 0.75hz).…”