A mean field approach to model levels of consciousness from EEG recordings

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.

Study at a glance

Abstract

We introduce a mean-field model for analysing the dynamics of human consciousness. In particular, inspired by the Giulio Tononi's Integrated Information Theory and by the Max Tegmark's representation of consciousness, we study order-disorder phase transitions on Curie-Weiss models generated by processing EEG signals. The latter have been recorded on healthy individuals undergoing deep sedation. Then, we implement a machine learning tool for classifying mental states using, as input, the critical temperatures computed in the Curie-Weiss models. Results show that, by the proposed method, it is possible to discriminate between states of awareness and states of deep sedation. Besides, we identify a state space for representing the path between mental states, whose dimensions correspond to critical temperatures computed over different frequency bands of the EEG signal. Beyond possible theoretical implications in the study of human consciousness, resulting from our model, we deem relevant to emphasise that the proposed method could be exploited for clinical applications.