Exploring Mental State Changes during Hypnotherapy using Adaptive Mixture Independent Component Analysis of EEG

Abstract

Advancing our understanding of neurocognitive systems impacted by hypnotherapy may improve therapeutic outcomes. This study addresses the challenge of decoding cortical state changes from continuous electroencephalographic (EEG) data recorded during hypnosis. We model changes in brain state dynamics over the course of hypnosis using Adaptive Mixture Independent Component Analysis (AMICA), an unsupervised approach that learns multiple ICA models for characterizing non-stationary, unlabeled data. Applied to EEG from six sessions of hypnosis, AMICA characterized changes in system-wide brain activity that corresponded to transitions between hypnosis stages. Moreover, the results showed consistent AMICA-based models across sessions and subjects that reflected distinct patterns of source activities in different hypnosis states. By analyzing independent component clusters associated with distinctive classes of model probability patterns, shifts in the theta, alpha, and other spectral features of source activities were characterized over the course of the therapy sessions. The AMICA approach offers a promising tool for linking brain-network changes during hypnotherapy with physiological and cognitive state changes brought about by this form of treatment. It can also ignite new research and developments toward brain-state monitoring for clinical applications.