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.

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.

AbstractCompared 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.MADE is freely available under the terms of the GNU General Public License at https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline.

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 using the international 10-20 system, but to our knowledge no such algorithms have been optimized for the geodesic sensor net, which is often used with infants and children. 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 pre-processing 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, it performed better than ICLabel for pediatric data.These results indicate that optimizing existing algorithms for data collected with a geodesic net improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations.

Error monitoring allows individuals to monitor and adapt their behavior by detecting errors. Error monitoring is thought to develop throughout childhood and adolescence. However, most of this evidence comes from studies in late childhood and adolescence utilizing event-related potentials (ERPs). The current study utilizes timefrequency (TF) and connectivity analyses to provide a comprehensive examination of age-related changes in error-monitoring processes across early childhood (N = 326; 50.9% females; 4-9 years). ERP analyses indicated the presence of the error-related negativity (ERN) and error positivity (Pe) across all ages. Results showed no errorspecific age-related changes in the ERN and the Pe. However, TF analyses suggested error-related frontocentral responses in delta and theta signal strength (power), delta consistency (intertrial phase synchrony), and delta synchrony (interchannel phase synchrony) between frontrocentral and frontolateral clusters-all of which increased with age. Additionally, the current study examines the reliability and effect size estimates of the ERP and TF measures. For most measures, more trials were needed to achieve acceptable reliability than what is commonly used in the psychophysiological literature. Resources to facilitate the measurement and reporting of reliability are provided.Overall, findings highlight the utility of TF analyses and provide useful information for future studies examining the development of error monitoring.

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The last decade has seen increased availability of mobile electroencephalography (EEG). These mobile systems enable researchers to conduct data collection “in‐context,” reducing participant burden and potentially increasing diversity and representation of research samples. Our research team completed in‐home data collection from more than 400 twelve‐month‐old infants from low‐income backgrounds using a mobile EEG system. In this paper, we provide methodological and analytic guidance for collecting high‐quality, mobile EEG in infants. Specifically, we offer insights and recommendations for equipment selection, data collection, and data analysis, highlighting important considerations for selecting a mobile EEG system. Examples include the size of the recording equipment, electrode type, reference types, and available montages. We also highlight important recommendations surrounding preparing a nonstandardized recording environment for EEG collection, obtaining informed consent from parents, instructions for parents during capping and recording, stimuli and task design, training researchers, and monitoring data as it comes in. Additionally, we provide access to the analysis code and demonstrate the robustness of the data from a recent study using this approach, in which 20 artifact‐free epochs achieve good internal consistency reliability. Finally, we provide recommendations and publicly available resources for future studies aiming to collect mobile EEG.

This study is the first to examine spectrum-wide (1 to 250 Hz) differences in electroencephalogram (EEG) power between eyes open (EO) and eyes closed (EC) resting state conditions in 486 children. The results extend the findings of previous studies by characterizing EEG power differences from 30 to 250 Hz between EO and EC across childhood. Developmental changes in EEG power showed spatial and frequency band differences as a function of age and EO/EC condition.A 64-electrode system was used to record EEG at 4, 5, 7, 9, and 11 years of age. Specific findings were: (1) the alpha peak shifts from 8 Hz at 4 years to 9 Hz at 11 years, (2) EC results in increased EEG power (compared to EO) at lower frequencies but decreased EEG power at higher frequencies for all ages, (3) the EEG power difference between EO and EC changes from positive to negative within a narrow frequency band which shifts toward higher frequencies with age, from 9 to 12 Hz at 4 years to 32 Hz at 11 years, (4) at all ages EC is characterized by an increase in lower frequency EEG power most prominently over posterior regions, (5) at all ages, during EC, decreases in EEG power above 30 Hz are mostly over anterior regions of the scalp. This report demonstrates that the simple challenge of opening and closing the eyes offers the potential to provide quantitative biomarkers of phenotypic variation in brain maturation by employing a brief, minimally invasive protocol throughout childhood.