The Maryland analysis of developmental EEG (MADE) pipeline
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...
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| Veröffentlicht in: | Psychophysiology Jg. 57; H. 6; S. e13580 - n/a |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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United States
Blackwell Publishing Ltd
01.06.2020
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| ISSN: | 0048-5772, 1469-8986, 1469-8986, 1540-5958 |
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| 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.
This article presents the Maryland Analysis of Developmental EEG (MADE) pipeline, an automated preprocessing pipeline for EEG data that can be used with EEG recorded with different systems, populations, levels of artifact contamination, and lengths of recording. MADE is based on MATLAB and includes customized features particularly useful for EEG data collected from pediatric populations. |
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| AbstractList | 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.
This article presents the Maryland Analysis of Developmental EEG (MADE) pipeline, an automated preprocessing pipeline for EEG data that can be used with EEG recorded with different systems, populations, levels of artifact contamination, and lengths of recording. MADE is based on MATLAB and includes customized features particularly useful for EEG data collected from pediatric populations. 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. 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 . This article presents the Maryland Analysis of Developmental EEG (MADE) pipeline, an automated preprocessing pipeline for EEG data that can be used with EEG recorded with different systems, populations, levels of artifact contamination, and lengths of recording. MADE is based on MATLAB and includes customized features particularly useful for EEG data collected from pediatric populations. 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.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. |
| Author | Buzzell, George A. Debnath, Ranjan Fox, Nathan A. Morales, Santiago Bowers, Maureen E. Leach, Stephanie C. |
| Author_xml | – sequence: 1 givenname: Ranjan orcidid: 0000-0001-6952-8426 surname: Debnath fullname: Debnath, Ranjan email: rdebnath@umd.edu organization: University of Maryland – sequence: 2 givenname: George A. surname: Buzzell fullname: Buzzell, George A. organization: University of Maryland – sequence: 3 givenname: Santiago orcidid: 0000-0002-9850-042X surname: Morales fullname: Morales, Santiago organization: University of Maryland – sequence: 4 givenname: Maureen E. surname: Bowers fullname: Bowers, Maureen E. organization: University of Maryland – sequence: 5 givenname: Stephanie C. surname: Leach fullname: Leach, Stephanie C. organization: University of Maryland – sequence: 6 givenname: Nathan A. surname: Fox fullname: Fox, Nathan A. organization: University of Maryland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32293719$$D View this record in MEDLINE/PubMed |
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| Copyright | 2020 Society for Psychophysiological Research 2020 Society for Psychophysiological Research. Copyright © 2020 by the Society for Psychophysiological Research |
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| Keywords | pipeline automated preprocessing EEG pediatric |
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| SubjectTerms | Adolescent automated Automation Child Child, Preschool Contamination Datasets as Topic EEG Electroencephalography Electroencephalography - methods Electroencephalography - standards Functional Neuroimaging - methods Functional Neuroimaging - standards Humans Infant pediatric Pediatrics pipeline Pipelines preprocessing Reproducibility of Results Signal Processing, Computer-Assisted Standardization |
| Title | The Maryland analysis of developmental EEG (MADE) pipeline |
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