Reality Bending Lab › Publications › Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

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

Lau, Z. J., Pham, T., Chen, S. H. A., & Makowski, D.

European Journal of Neuroscience, 2022

Complexity measures — entropy, fractal dimension, predictability — are increasingly applied to EEG, and their names hide what they actually compute. This review explains them in plain terms, sorts them into measures of predictability and of regularity, and synthesises what they have shown across consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative conditions, and the lifespan.

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.

• DOI: 10.1111/EJN.15800
• Full text (PDF)

Keywords: Complexity Science · Signal Processing · Psychophysiology

Cite

Lau, Z. J., Pham, T., Chen, S. H. A., & 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. https://doi.org/10.1111/EJN.15800

See also

• The Structure of Chaos: An Empirical Comparison of Fractal Physiology Complexity Indices Using NeuroKit2 — Makowski, D., Te, A. S., Pham, T., Lau, Z. J., & Chen, S. H. A. (2022)
• Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial — Pham, T., Lau, Z. J., Chen, S. H. A., & Makowski, D. (2021)
• NeuroKit2: A Python toolbox for neurophysiological signal processing — Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H., Schölzel, C., & Chen, S. H. A. (2021)