Journal
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Volume 2020, Issue 8, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1742-5468/ababfb
Keywords
computational neuroscience; classical phase transitions
Categories
Funding
- Belgian National Funds for Scientific Research (F.R.SFNRS)
- European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant [945539]
- University Hospital of Liege
- fund Generet
- King Baudouin Foundation
- BIAL Foundation
- AstraZeneca foundation
- Belgian Federal Science Policy Office (BELSPO)
- Center-TBI project [FP7-HEALTH-602150]
- Public Utility Foundation 'Universite Europeenne du Travail'
- Fondazione Europea di Ricerca Biomedica
- Mind Science Foundation
- DOCMA project [EU-H2020-MSCA-RISE-778234]
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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.
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