Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

Summary

EEG complexity measures, which quantify the predictability and irregularity of neural signals, are increasingly used as biomarkers for psychopathologies like depression and schizophrenia. This review explains these measures in accessible terms, categorizing them into those assessing predictability and regularity. It synthesizes findings across consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative conditions, and lifespan changes, addressing theoretical and methodological issues that cause data discrepancies. The review also provides guidance on selecting and interpreting these metrics for psychological and neuropsychiatric research.

Study at a glance

Abstract

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.