{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T19:16:00Z","timestamp":1790363760054,"version":"4.1.0"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:00:00Z","timestamp":1622419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["813483"],"award-info":[{"award-number":["813483"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002342","name":"Suomalainen Tiedeakatemia","doi-asserted-by":"publisher","award":["313242"],"award-info":[{"award-number":["313242"]}],"id":[{"id":"10.13039\/501100002342","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002342","name":"Suomalainen Tiedeakatemia","doi-asserted-by":"publisher","award":["288220"],"award-info":[{"award-number":["288220"]}],"id":[{"id":"10.13039\/501100002342","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002342","name":"Suomalainen Tiedeakatemia","doi-asserted-by":"publisher","award":["3104450"],"award-info":[{"award-number":["3104450"]}],"id":[{"id":"10.13039\/501100002342","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008320","name":"Suomen Aivos\u00e4\u00e4ti\u00f6","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008320","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["MOP-133710"],"award-info":[{"award-number":["MOP-133710"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["PJT-166076"],"award-info":[{"award-number":["PJT-166076"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 HD101419"],"award-info":[{"award-number":["R01 HD101419"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006126","name":"Hospital for Sick Children","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006126","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["APP1144936"],"award-info":[{"award-number":["APP1144936"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["APP2002135"],"award-info":[{"award-number":["APP2002135"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005744","name":"Lastentautien Tutkimuss\u00e4\u00e4ti\u00f6","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005744","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006306","name":"Sigrid Jus\u00e9liuksen S\u00e4\u00e4ti\u00f6","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006306","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:p>Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8\u201316 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81\u2013100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.<\/jats:p>","DOI":"10.3389\/fnhum.2021.675154","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T04:33:53Z","timestamp":1622435633000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization"],"prefix":"10.3389","volume":"15","author":[{"given":"Saeed","family":"Montazeri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elana","family":"Pinchefsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilse","family":"Tse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viviana","family":"Marchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jukka","family":"Kohonen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minna","family":"Kauppila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manu","family":"Airaksinen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karoliina","family":"Tapani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P\u00e4ivi","family":"Nevalainen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cecil","family":"Hahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily W. Y.","family":"Tam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan J.","family":"Stevenson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sampsa","family":"Vanhatalo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,5,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.clinph.2015.05.024","article-title":"Grading hypoxic\u2013ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine.","volume":"127","author":"Ahmed","year":"2016","journal-title":"Clin. Neurophysiol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"3014","DOI":"10.1016\/j.clinph.2016.06.018","article-title":"Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor.","volume":"127","author":"Ansari","year":"2016","journal-title":"Clin. Neurophysiol."},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab5469","article-title":"A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants.","volume":"17","author":"Ansari","year":"2020","journal-title":"J. Neural Eng."},{"key":"B4","first-page":"1","article-title":"Self-supervised representation learning from electroencephalography signals","author":"Banville","year":"2019","journal-title":"Proceedings of the 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abca18","article-title":"Uncovering the structure of clinical EEG signals with self-supervised learning.","volume":"18","author":"Banville","year":"2020","journal-title":"J. Neural Eng."},{"key":"B6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/s0167-7012(00)00201-3","article-title":"Artificial neural networks: fundamentals, computing, design, and application.","volume":"43","author":"Basheer","year":"2000","journal-title":"J. Microbiol. Methods"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1089\/heat.2019.0006","article-title":"Deep learning in orthopedics: how do we build trust in the machine?","author":"Borjali","year":"2020","journal-title":"Healthc. Transform Artif. Intell. Autom. Rob."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1111\/j.1651-2227.2010.01809.x","article-title":"An international survey of EEG use in the neonatal intensive care unit.","volume":"99","author":"Boylan","year":"2010","journal-title":"Acta Paediatr."},{"key":"B9","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.siny.2013.04.004","article-title":"Monitoring neonatal seizures.","volume":"18","author":"Boylan","year":"2013","journal-title":"Semin. Fetal Neonatal Med."},{"key":"B10","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique.","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"B11","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neucom.2019.04.058","article-title":"A deep learning framework for identifying children with ADHD using an EEG-based brain network.","volume":"356","author":"Chen","year":"2019","journal-title":"Neurocomputing"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1016\/j.clinph.2011.01.043","article-title":"Validation of a new automated neonatal seizure detection system: a clinician\u2019s perspective.","volume":"122","author":"Cherian","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"B13","doi-asserted-by":"publisher","first-page":"F201","DOI":"10.1136\/adc.2004.062745","article-title":"Role of cerebral function monitoring in the newborn.","volume":"90","author":"de Vries","year":"2005","journal-title":"Arch. Dis. Child. Fetal Neonatal Ed."},{"key":"B14","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1016\/j.clinph.2017.06.250","article-title":"Interrater agreement in visual scoring of neonatal seizures based on majority voting on a web-based system: the neoguard EEG database.","volume":"128","author":"Dereymaeker","year":"2017","journal-title":"Clin. Neurophysiol."},{"key":"B15","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/CEC.2000.870311","article-title":"A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator","author":"Emmanouilidis","year":"2000","journal-title":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512)"},{"key":"B16","doi-asserted-by":"publisher","first-page":"721","DOI":"10.32604\/cmes.2020.07470","article-title":"Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG.","volume":"122","author":"Guo","year":"2020","journal-title":"Comput. Model. Eng. Sci."},{"key":"B17","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/B978-1-55860-335-6.50023-4","article-title":"Irrelevant features and the subset selection problem","author":"John","year":"1994","journal-title":"Machine Learning Proceedings 1994"},{"key":"B18","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.clinph.2020.09.024","article-title":"Abnormalities in evoked potentials associated with abnormal glycemia and brain injury in neonatal hypoxic-ischemic encephalopathy.","volume":"132","author":"Kamino","year":"2021","journal-title":"Clin. Neurophysiol."},{"key":"B19","first-page":"221","article-title":"Artifact detection in neonatal EEG using Gaussian mixture models","author":"Kauppila","year":"2017","journal-title":"EMBEC &amp; NBC 2017"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.1016\/j.clinph.2010.12.059","article-title":"Quantitative EEG analysis in neonatal hypoxic ischaemic encephalopathy.","volume":"122","author":"Korotchikova","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"B21","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/S0031-3203(99)00041-2","article-title":"Comparison of algorithms that select features for pattern classifiers.","volume":"33","author":"Kudo","year":"2000","journal-title":"Pattern Recognit."},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2431-13-194","article-title":"Prevalence and etiology of false normal aEEG recordings in neonatal hypoxic-ischaemic encephalopathy.","volume":"13","author":"Marics","year":"2013","journal-title":"BMC Pediatr."},{"key":"B23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/WNP.0000000000000534","article-title":"Interrater and intrarater agreement in neonatal electroencephalogram background scoring.","volume":"36","author":"Massey","year":"2019","journal-title":"J. Clin. Neurophysiol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/TBME.2015.2477946","article-title":"Improving reliability of monitoring background EEG dynamics in asphyxiated infants.","volume":"63","author":"Mati\u0107","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/11\/6\/066007","article-title":"Holistic approach for automated background EEG assessment in asphyxiated full-term infants.","volume":"11","author":"Matic","year":"2014","journal-title":"J. Neural Eng."},{"key":"B26","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/s0887-8994(02)00396-x","article-title":"Prognostic value of neonatal discontinuous EEG.","volume":"27","author":"Menache","year":"2002","journal-title":"Pediatr. Neurol."},{"key":"B27","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/0013-4694(72)90063-6","article-title":"The neonatal EEG: statistical studies and prognostic value in full-term and pre-term babies.","volume":"32","author":"Monod","year":"1972","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"B28","doi-asserted-by":"publisher","first-page":"F187","DOI":"10.1136\/adc.2005.086314","article-title":"Defining the gap between electrographic seizure burden, clinical expression and staff recognition of neonatal seizures.","volume":"93","author":"Murray","year":"2008","journal-title":"Arch. Dis. Child. Fetal Neonatal Ed."},{"key":"B29","doi-asserted-by":"publisher","first-page":"e459","DOI":"10.1542\/peds.2008-2190","article-title":"Early EEG findings in hypoxic-ischemic encephalopathy predict outcomes at 2 years.","volume":"124","author":"Murray","year":"2009","journal-title":"Pediatrics"},{"key":"B30","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s00381-003-0809-y","article-title":"Progressive posthemorrhagic hydrocephalus leads to changes of amplitude-integrated EEG activity in preterm infants.","volume":"20","author":"Olischar","year":"2004","journal-title":"Childs Nerv. Syst."},{"key":"B31","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1142\/S021800140300271X","article-title":"A methodology for feature selection using multiobjective genetic algorithms for handwritten digit string recognition.","volume":"17","author":"Oliveira","year":"2003","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"B32","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1542\/peds.2004-0863","article-title":"Sleep-wake cycling on amplitude-integrated electroencephalography in term newborns with hypoxic-ischemic encephalopathy.","volume":"115","author":"Osredkar","year":"2005","journal-title":"Pediatrics"},{"key":"B33","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.jpeds.2019.02.027","article-title":"Hyperglycemia and glucose variability are associated with worse brain function and seizures in neonatal encephalopathy: a prospective cohort study.","volume":"209","author":"Pinchefsky","year":"2019","journal-title":"J. Pediatr."},{"key":"B34","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1080\/08874417.1996.11647233","article-title":"Data conflict elimination in neural network training.","volume":"36","author":"Raggad","year":"1996","journal-title":"J. Comput. Inf. Syst."},{"key":"B35","article-title":"Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network","author":"Raurale","year":"2020","journal-title":"Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abe8ae","article-title":"Grading hypoxic-ischemic encephalopathy in neonatal EEG with convolutional neural networks and quadratic time\u2013frequency distributions.","volume":"18","author":"Raurale","year":"2021","journal-title":"J. Neural Eng."},{"key":"B37","first-page":"4125","article-title":"Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG","author":"Raurale","year":"2019","journal-title":"Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab260c","article-title":"Deep learning-based electroencephalography analysis: a systematic review.","volume":"16","author":"Roy","year":"2019","journal-title":"J. Neural Eng."},{"key":"B39","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1142\/9789814343138_0005","article-title":"A note on genetic algorithms for large-scale feature selection","author":"Siedlecki","year":"1993","journal-title":"Handbook of Pattern Recognition and Computer Vision"},{"key":"B40","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/s10439-012-0710-5","article-title":"An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy.","volume":"41","author":"Stevenson","year":"2013","journal-title":"Ann. Biomed. Eng."},{"key":"B41","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.clinph.2017.10.031","article-title":"The effect of reducing EEG electrode number on the visual interpretation of the human expert for neonatal seizure detection.","volume":"129","author":"Stevenson","year":"2018","journal-title":"Clin. Neurophysiol."},{"key":"B42","first-page":"926","article-title":"Artefact detection in neonatal EEG","author":"Stevenson","year":"2014","journal-title":"Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"key":"B43","doi-asserted-by":"publisher","first-page":"1564","DOI":"10.1002\/acn3.51132","article-title":"Reliability and accuracy of EEG interpretation for estimating age in preterm infants.","volume":"7","author":"Stevenson","year":"2020","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"B44","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.dss.2014.12.006","article-title":"Clinical implementation of a neonatal seizure detection algorithm.","volume":"70","author":"Temko","year":"2015","journal-title":"Decis. Support Syst."},{"key":"B45","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1080\/08035250410022882","article-title":"Burst suppression on amplitude\u2212integrated electroencephalogram may be induced by midazolam: a report on three cases.","volume":"93","author":"Ter Horst","year":"2004","journal-title":"Acta Paediatr."},{"key":"B46","doi-asserted-by":"publisher","first-page":"e131","DOI":"10.1542\/peds.2009-2938","article-title":"Effect of hypothermia on amplitude-integrated electroencephalogram in infants with asphyxia.","volume":"126","author":"Thoresen","year":"2010","journal-title":"Pediatrics"},{"key":"B47","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.earlhumdev.2008.11.007","article-title":"Brain monitoring in neonates.","volume":"85","author":"Toet","year":"2009","journal-title":"Early Hum. Dev."},{"key":"B48","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1097\/WNP.0b013e3182872b24","article-title":"American clinical neurophysiology society standardized EEG terminology and categorization for the description of continuous EEG monitoring in neonates: report of the American Clinical Neurophysiology Society critical care monitoring committee.","volume":"30","author":"Tsuchida","year":"2013","journal-title":"J. Clin. Neurophysiol."},{"key":"B49","first-page":"21","article-title":"Understanding conflicting data.","volume":"10","author":"Versaggi","year":"1995","journal-title":"AI Expert"},{"key":"B50","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/s0387-7604(99)00034-0","article-title":"Neonatal EEG: a powerful tool in the assessment of brain damage in preterm infants.","volume":"21","author":"Watanabe","year":"1999","journal-title":"Brain Dev."},{"key":"B51","doi-asserted-by":"publisher","first-page":"F383","DOI":"10.1136\/archdischild-2016-311770","article-title":"Severe hypercapnia causes reversible depression of aEEG background activity in neonates: an observational study.","volume":"102","author":"Weeke","year":"2017","journal-title":"Arch. Dis. Child. Fetal Neonatal Ed."},{"key":"B52","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","article-title":"A learning algorithm for continually running fully recurrent neural networks.","volume":"1","author":"Williams","year":"1989","journal-title":"Neural Comput."},{"key":"B53","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1111\/epi.13661","article-title":"Interrater agreement in the interpretation of neonatal electroencephalography in hypoxic-ischemic encephalopathy.","volume":"58","author":"Wusthoff","year":"2017","journal-title":"Epilepsia"}],"updated-by":[{"DOI":"10.3389\/fnhum.2024.1417744","type":"corrigendum","label":"Corrigendum","source":"publisher","updated":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000}}],"container-title":["Frontiers in Human Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2021.675154\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T14:25:21Z","timestamp":1714141521000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2021.675154\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,31]]},"references-count":53,"alternative-id":["10.3389\/fnhum.2021.675154"],"URL":"https:\/\/doi.org\/10.3389\/fnhum.2021.675154","relation":{"corrigendum":[{"id-type":"doi","id":"10.3389\/fnhum.2024.1417744","asserted-by":"object"}]},"ISSN":["1662-5161"],"issn-type":[{"value":"1662-5161","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,31]]},"article-number":"675154"}}