A mean field approach to model levels of consciousness from EEG recordings
arXiv Preprint Archive February 6, 2020 Marco Alberto Javarone, Olivia Gosseries, Daniele Marinazzo et al.
A mean-field model inspired by Integrated Information Theory and Tegmark's representation of consciousness analyzes order-disorder phase transitions on Curie-Weiss models generated from EEG signals recorded on healthy individuals undergoing deep sedation. A machine learning tool classifies mental states using critical temperatures computed from these models. The method discriminates between states of awareness and deep sedation. A state space representing the path between mental states is identified, with dimensions corresponding to critical temperatures over different EEG frequency bands. The method may have clinical applications.