Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD

Abstract Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symp...

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Autores principales:	Safaa Eldeeb, Busra T. Susam, Murat Akcakaya, Caitlin M. Conner, Susan W. White, Carla A. Mazefsky
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/59b7931393d84db7be84ce7f307ed048
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spelling	oai:doaj.org-article:59b7931393d84db7be84ce7f307ed0482021-12-02T17:05:45ZTrial by trial EEG based BCI for distress versus non distress classification in individuals with ASD10.1038/s41598-021-85362-82045-2322https://doaj.org/article/59b7931393d84db7be84ce7f307ed0482021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85362-8https://doaj.org/toc/2045-2322Abstract Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).Safaa EldeebBusra T. SusamMurat AkcakayaCaitlin M. ConnerSusan W. WhiteCarla A. MazefskyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Safaa Eldeeb Busra T. Susam Murat Akcakaya Caitlin M. Conner Susan W. White Carla A. Mazefsky Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD
description	Abstract Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).
format	article
author	Safaa Eldeeb Busra T. Susam Murat Akcakaya Caitlin M. Conner Susan W. White Carla A. Mazefsky
author_facet	Safaa Eldeeb Busra T. Susam Murat Akcakaya Caitlin M. Conner Susan W. White Carla A. Mazefsky
author_sort	Safaa Eldeeb
title	Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD
title_short	Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD
title_full	Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD
title_fullStr	Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD
title_full_unstemmed	Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD
title_sort	trial by trial eeg based bci for distress versus non distress classification in individuals with asd
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/59b7931393d84db7be84ce7f307ed048
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Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD

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