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Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise

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dc.contributor.authorSusam, Busra T.
dc.contributor.authorRiek, Nathan T.
dc.contributor.authorBeck, Kelly
dc.contributor.authorEldeeb, Safaa
dc.contributor.authorHudac, Caitlin M.
dc.contributor.authorGable, Philip A.
dc.contributor.authorConner, Caitlin
dc.contributor.authorAkcakaya, Murat
dc.contributor.authorWhite, Susan W.
dc.contributor.authorMazefsky, Carla
dc.contributor.otherUniversity of Pittsburgh
dc.contributor.otherUniversity of South Carolina Columbia
dc.contributor.otherUniversity of Delaware
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2023-09-28T19:11:26Z
dc.date.available2023-09-28T19:11:26Z
dc.date.issued2022
dc.description.abstractMindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report tools. Electroencephalography (EEG) has successfully quantified neural responses to emotional arousal and meditation in other populations, making it ideal to objectively measure neural responses before and after mindfulness (MF) practice among individuals with ASD. We performed an EEG-based analysis during a resting state paradigm in 35 youth with ASD. Specifically, we developed a machine learning classifier and a feature and channel selection approach that separates resting states preceding (Pre-MF) and following (Post-MF) a mindfulness meditation exercise within participants. Across individuals, frontal and temporal channels were most informative. Total power in the beta band (16-30 Hz), Total power (4-30 Hz), relative power in alpha band (8-12 Hz) were the most informative EEG features. A classifier using a non-linear combination of selected EEG features from selected channel locations separated Pre-MF and Post-MF resting states with an average accuracy, sensitivity, and specificity of 80.76%, 78.24%, and 82.14% respectively. Finally, we validated that separation between Pre-MF and Post-MF is due to the MF prime rather than linear-temporal drift. This work underscores machine learning as a critical tool for separating distinct resting states within youth with ASD and will enable better classification of underlying neural responses following brief MF meditation.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationSusam, B. T., Riek, N. T., Beck, K., Eldeeb, S., Hudac, C. M., Gable, P. A., Conner, C., Akcakaya, M., White, S., & Mazefsky, C. (2022). Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise. In IEEE Transactions on Neural Systems and Rehabilitation Engineering (Vol. 30, pp. 2395–2405). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tnsre.2022.3199151
dc.identifier.doi10.1109/TNSRE.2022.3199151
dc.identifier.orcidhttps://orcid.org/0000-0002-6224-2086
dc.identifier.orcidhttps://orcid.org/0000-0001-5094-1931
dc.identifier.orcidhttps://orcid.org/0000-0001-8250-2044
dc.identifier.orcidhttps://orcid.org/0000-0003-3286-560X
dc.identifier.urihttps://ir.ua.edu/handle/123456789/10977
dc.languageEnglish
dc.language.isoen_US
dc.publisherIEEE
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectTask analysis
dc.subjectElectroencephalography
dc.subjectGames
dc.subjectAutism
dc.subjectSociology
dc.subjectStars
dc.subjectRegulation
dc.subjectEEG
dc.subjectmindfulness
dc.subjectresting-state
dc.subjectAUTISM SPECTRUM DISORDER
dc.subjectEMOTION REGULATION
dc.subjectINTERVENTIONS
dc.subjectINDIVIDUALS
dc.subjectTHERAPY
dc.subjectADULTS
dc.subjectSTATE
dc.subjectEngineering, Biomedical
dc.subjectRehabilitation
dc.titleQuantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exerciseen_US
dc.typeArticle
dc.typetext

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