Information Geometry Theoretic Measures for Characterizing Neural Information Processing from Simulated EEG Signals

Jia-Chen Hua, Eun-jin Kim, Fei He

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Abstract

In this work, we explore information geometry theoretic measures for characterizing neural information processing from EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer’s disease (AD) patients with both eyes-closed and eyes-open conditions. In particular, we employ information rates to quantify the time evolution of probability density functions of simulated EEG signals, and employ causal information rates to quantify one signal’s instantaneous influence on another signal’s information rate. These two measures help us find significant and interesting distinctions between healthy subjects and AD patients when they open or close their eyes. These distinctions may be further related to differences in neural information processing activities of the corresponding brain regions, and to differences in connectivities among these brain regions. Our results show that information rate and causal information rate are superior to their more traditional or established information-theoretic counterparts, i.e., differential entropy and transfer entropy, respectively. Since these novel, information geometry theoretic measures can be applied to experimental EEG signals in a model-free manner, and they are capable of quantifying non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity presented in real-world EEG signals, we believe that they can form an important and powerful tool-set for both understanding neural information processing in the brain and the diagnosis of neurological disorders, such as Alzheimer’s disease as presented in this work.
Original languageEnglish
Article number213
Number of pages61
JournalEntropy
Volume26
Issue number3
Early online date28 Feb 2024
DOIs
Publication statusPublished - Mar 2024

Bibliographical note

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Funder

This work was supported by the EPSRC Grant EP/W036770/1 (https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/W036770/1), accessed on 17 February 2024.

Keywords

  • information geometry
  • information length
  • information rate
  • causal information rate
  • causality
  • stochastic oscillators
  • electroencephalography
  • stochastic simulation
  • signal processing
  • dementia
  • Alzheimer’s disease
  • information theory
  • neural information processing
  • brain networks

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