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

Javarone, MA; Gosseries, O; Marinazzo, D; Noirhomme, Q; Bonhomme, V; Laureys, S; Chennu, S; (2020) A mean field approach to model levels of consciousness from EEG recordings. Journal of Statistical Mechanics: Theory and Experiment , 2020 , Article 083405. 10.1088/1742-5468/ababfb.

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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.

Type: Article
Title: A mean field approach to model levels of consciousness from EEG recordings
DOI: 10.1088/1742-5468/ababfb
Publisher version: http://dx.doi.org/10.1088/1742-5468/ababfb
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics > Clinical Operational Research Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10108747
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