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 customizable 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 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.

Mu desynchronization is the attenuation of EEG power in the alpha frequency range recorded over central scalp locations thought to reflect motor cortex activation. Mu desynchronization during observation of an action is believed to reflect mirroring system activation in humans. However, this notion has recently been questioned because, among other reasons, the potential contamination of mu rhythm and occipital alpha activity induced by attention processes following presentation of visual stimuli in observation conditions. This study examined the validity of mu desynchronization as a measure of mirroring system activation in infants and further investigated the pattern of functional connectivity between the central and occipital regions during execution and observation of movement. EEG was recorded while 46 9-monthold infants executed grasping actions and observed an experimenter grasping. Current source density (CSD) was applied to EEG data and, time-frequency and connectivity analyses were performed in CSD transformed data. Mu desynchronization was evident over central regions during both execution and observation of movements. Independent alpha desynchronization over occipital region was also present in both conditions. The connectivity analyses revealed that central-occipital areas were functionally more connected compared to other areas of the brain during observation of movements. Collectively, the results demonstrate the validity of mu desynchronization as an index of infant mirroring system activity and support the proposal of a functional connection between distinct mirroring and attention processes during observation of action.

A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Independent component analysis (ICA) can separate artifactual and neural activity, allowing researchers to remove such artifactual activity and retain a greater percentage of EEG data for analyses. However, manual identification of artifactual components is time‐consuming and requires subjective judgment. Automated algorithms, like ADJUST and ICLabel, have been validated on adults, but to our knowledge, no such algorithms have been optimized for pediatric data. Therefore, in an attempt to automate artifact selection for pediatric data collected with geodesic nets, we modified ADJUST's algorithm. Our “adjusted‐ADJUST” algorithm was compared to the “original‐ADJUST” algorithm and ICLabel in adults, children, and infants on three different performance measures: respective classification agreement with expert coders, the number of trials retained following artifact removal, and the reliability of the EEG signal after preprocessing with each algorithm. Overall, the adjusted‐ADJUST algorithm performed better than the original‐ADJUST algorithm and no ICA correction with adult and pediatric data. Moreover, in some measures, it performed better than ICLabel for pediatric data. These results indicate that optimizing existing algorithms improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations. Adjusted‐ADJUST is freely available under the terms of the GNU General Public License at: https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline/tree/master/adjusted_adjust_scripts.

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