  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.
