Beyond linear measures: Revealing hidden neural dynamics in sports and exercise cognition with non-linear EEG.

Over the past fifty years, cognitive neuroscience has greatly enriched our understanding of how sport and exercise influence the human brain. Electroencephalography (EEG), with its non-invasive nature and exceptional temporal resolution, has been instrumental in uncovering the neurocognitive processes linking physical exercise and sports to cognition. While traditional linear EEG analyses have yielded valuable insights, they are limited in capturing the complex non-linear dynamics inherent in EEG signals, potentially obscuring critical aspects of brain-behavior relationships. This perspective article first reviews contemporary research using linear EEG methods to examine neural correlates as both antecedents and consequences of sport and exercise behaviors. We then discuss the non-linear nature of brain dynamics and the advantages of non-linear EEG analyses-such as multiscale entropy (MSE)-for revealing adaptive neural changes induced by physical activity. Finally, we introduce two advanced non-linear EEG techniques, cross-frequency coupling (CFC) and Holo-Hilbert Spectral Analysis (HHSA), which, though yet to be applied in sports and exercise science, hold significant promise for uncovering complex neural mechanisms overlooked by traditional linear approaches. Advocating for the integration of non-linear EEG analyses alongside conventional methods, this review aims to provide a more comprehensive understanding of the neural underpinnings of sport, exercise, and cognition, paving the way for future research in this evolving field.

• Publication year

  2025

• Venue

  Biological Psychology

• Publication date

  2025-09-01

• Fields of study

  Medicine, Psychology

• Identifiers
  DOI 10.1016/j.biopsycho.2025.109126PMID 40935281
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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