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.
A framework using electroencephalography (EEG) and nonlinear analysis methods can differentiate four states of consciousness: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. Permutation entropy (PE) distinguished all four states. Altered contents of consciousness were best differentiated by sample entropy (SampEn) and permutation Lempel-Ziv complexity (PLZC), while levels of consciousness were best differentiated by relative power of Gamma and PE. A multi-dimensional index combining PE, PLZC, SampEn, and detrended fluctuation analysis (DFA) achieved 92.3% classification accuracy using a genetic algorithm-based support vector machine (GA-SVM), outperforming random forest and neural networks. A multivariable linear regression model constructed coordinate values for level and content dimensions.