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

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2022, Vol.30, p.2395-2405
• Main Authors: Susam, Busra T., Riek, Nathan T., Beck, Kelly, Eldeeb, Safaa, Hudac, Caitlin M., Gable, Philip A., Conner, Caitlin, Akcakaya, Murat, White, Susan, Mazefsky, Carla
• Format: Article
• Language: English
• Subjects: Adolescent; Arousal; Autism; Autism Spectrum Disorder; Classifiers; EEG; Electroencephalography; Emotional regulation; Emotions; Empirical analysis; Games; Humans; Learning algorithms; Machine learning; Meditation; Mindfulness; Regulation; resting-state; Sociology; Stars; Task analysis
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2022.3199151

Mindfulness 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.