Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163861
Title: Brain entropy, fractal dimensions and predictability: a review of complexity measures for EEG in healthy and neuropsychiatric populations
Authors: Lau, Zen Juen
Pham, Tam
Chen, Annabel Shen-Hsing
Makowski, Dominique
Keywords: Science::Medicine
Social sciences::Psychology
Issue Date: 2022
Source: Lau, Z. J., Pham, T., Chen, A. S. & Makowski, D. (2022). Brain entropy, fractal dimensions and predictability: a review of complexity measures for EEG in healthy and neuropsychiatric populations. European Journal of Neuroscience, 56(7), 5047-5069. https://dx.doi.org/10.1111/ejn.15800
Journal: European Journal of Neuroscience 
Abstract: There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
URI: https://hdl.handle.net/10356/163861
ISSN: 0953-816X
DOI: 10.1111/ejn.15800
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
School of Social Sciences 
National Institute of Education 
Research Centres: Centre for Research and Development in Learning (CRADLE) 
Rights: © 2022 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivsLicense, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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