Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise
IEEE transactions on neural systems and rehabilitation engineering August 17, 2022 Busra T. Susam, N. Riek, Kelly B. Beck et al.
A machine learning classifier applied to EEG data from 35 youth with Autism Spectrum Disorder distinguished resting brain states before and after a brief mindfulness meditation exercise with 80.76% accuracy. Frontal and temporal brain channels, along with beta-band total power, total power across 4–30 Hz, and relative alpha-band power, were the most informative features. The separation was confirmed to result from the mindfulness practice rather than from temporal drift. The findings demonstrate that EEG combined with machine learning can objectively measure neural changes following mindfulness meditation in this population, offering a way to assess emotion-regulation improvements without relying solely on self-report.