{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:49:52Z","timestamp":1760143792894,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"publisher","award":["EP\/W036770\/1"],"award-info":[{"award-number":["EP\/W036770\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2019s 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\u2019s instantaneous influence on another signal\u2019s 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\u2019s disease as presented in this work.<\/jats:p>","DOI":"10.3390\/e26030213","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T10:37:36Z","timestamp":1709116656000},"page":"213","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Information Geometry Theoretic Measures for Characterizing Neural Information Processing from Simulated EEG Signals"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6591-1417","authenticated-orcid":false,"given":"Jia-Chen","family":"Hua","sequence":"first","affiliation":[{"name":"Centre for Fluid and Complex Systems, Coventry University, Coventry CV1 2NL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5607-6635","authenticated-orcid":false,"given":"Eun-jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Centre for Fluid and Complex Systems, Coventry University, Coventry CV1 2NL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-6674","authenticated-orcid":false,"given":"Fei","family":"He","sequence":"additional","affiliation":[{"name":"Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2TL, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.3389\/fncom.2015.00048","article-title":"Stochastic Non-Linear Oscillator Models of EEG: The Alzheimer\u2019s Disease Case","volume":"9","author":"Ghorbanian","year":"2015","journal-title":"Front. 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