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NEAR: An artifact removal pipeline for human newborn EEG data
Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8800139/ https://www.ncbi.nlm.nih.gov/pubmed/35085870 http://dx.doi.org/10.1016/j.dcn.2022.101068 |
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author | Kumaravel, Velu Prabhakar Farella, Elisabetta Parise, Eugenio Buiatti, Marco |
author_facet | Kumaravel, Velu Prabhakar Farella, Elisabetta Parise, Eugenio Buiatti, Marco |
author_sort | Kumaravel, Velu Prabhakar |
collection | PubMed |
description | Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’ limited attentional span and much noisier due to non-stereotyped artifacts mainly caused by uncontrollable movements. We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns. NEAR is based on two key steps: 1) A novel bad channel detection tool based on the Local Outlier Factor (LOF), a robust outlier detection algorithm; 2) A parameter calibration procedure for adapting to newborn EEG data the algorithm Artifacts Subspace Reconstruction (ASR), developed for artifact removal in mobile adult EEG. Tests on simulated data showed that NEAR outperforms existing methods in removing representative newborn non-stereotypical artifacts. NEAR was validated on two developmental populations (newborns and 9-month-old infants) recorded with two different experimental designs (frequency-tagging and ERP). Results show that NEAR artifact removal successfully reproduces established EEG responses from noisy datasets, with a higher statistical significance than the one obtained by existing artifact removal methods. The EEGLAB-based NEAR pipeline is freely available at https://github.com/vpKumaravel/NEAR. |
format | Online Article Text |
id | pubmed-8800139 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-88001392022-02-02 NEAR: An artifact removal pipeline for human newborn EEG data Kumaravel, Velu Prabhakar Farella, Elisabetta Parise, Eugenio Buiatti, Marco Dev Cogn Neurosci Articles from the Special Issue on EEG Methods for Developmental Cognitive Neuroscientists: A Tutorial Approach; Edited by George Buzzell; Emilio Valadez; Santiago Morales; Nathan Fox; Sabine Hunnius Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’ limited attentional span and much noisier due to non-stereotyped artifacts mainly caused by uncontrollable movements. We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns. NEAR is based on two key steps: 1) A novel bad channel detection tool based on the Local Outlier Factor (LOF), a robust outlier detection algorithm; 2) A parameter calibration procedure for adapting to newborn EEG data the algorithm Artifacts Subspace Reconstruction (ASR), developed for artifact removal in mobile adult EEG. Tests on simulated data showed that NEAR outperforms existing methods in removing representative newborn non-stereotypical artifacts. NEAR was validated on two developmental populations (newborns and 9-month-old infants) recorded with two different experimental designs (frequency-tagging and ERP). Results show that NEAR artifact removal successfully reproduces established EEG responses from noisy datasets, with a higher statistical significance than the one obtained by existing artifact removal methods. The EEGLAB-based NEAR pipeline is freely available at https://github.com/vpKumaravel/NEAR. Elsevier 2022-01-15 /pmc/articles/PMC8800139/ /pubmed/35085870 http://dx.doi.org/10.1016/j.dcn.2022.101068 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Articles from the Special Issue on EEG Methods for Developmental Cognitive Neuroscientists: A Tutorial Approach; Edited by George Buzzell; Emilio Valadez; Santiago Morales; Nathan Fox; Sabine Hunnius Kumaravel, Velu Prabhakar Farella, Elisabetta Parise, Eugenio Buiatti, Marco NEAR: An artifact removal pipeline for human newborn EEG data |
title | NEAR: An artifact removal pipeline for human newborn EEG data |
title_full | NEAR: An artifact removal pipeline for human newborn EEG data |
title_fullStr | NEAR: An artifact removal pipeline for human newborn EEG data |
title_full_unstemmed | NEAR: An artifact removal pipeline for human newborn EEG data |
title_short | NEAR: An artifact removal pipeline for human newborn EEG data |
title_sort | near: an artifact removal pipeline for human newborn eeg data |
topic | Articles from the Special Issue on EEG Methods for Developmental Cognitive Neuroscientists: A Tutorial Approach; Edited by George Buzzell; Emilio Valadez; Santiago Morales; Nathan Fox; Sabine Hunnius |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8800139/ https://www.ncbi.nlm.nih.gov/pubmed/35085870 http://dx.doi.org/10.1016/j.dcn.2022.101068 |
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