Methodological considerations for studying neural oscillations
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Abstract
Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modelling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology and mechanisms of cognition. However, despite their prevalence, there are many concerns—new and old—about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analysing neural oscillations. The considerations are to (1) verify the presence of oscillations, as they may be absent; (2) validate oscillation band definitions, to address variable peak frequencies; (3) account for concurrent non‐oscillatory aperiodic activity, which might otherwise confound measures; measure and account for (4) temporal variability and (5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; (6) separate spatially overlapping rhythms, which may interfere with each other; and (7) consider the required signal‐to‐noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.
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Recent research suggests that age-changes in EEG power may be better characterized as changes in peak frequency or aperiodic activity.
“recent research suggests that age-changes in EEG power may be better characterized as changes in peak frequency or aperiodic activity (i.e., the arrhythmic component of neural oscillations) (Cellier et al., 2021, Donoghue et al., 2022).”
From Resting brain activity in early childhood predicts IQ at 18 years (2023) · cites this paper as Donoghue et al., 2022
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- Kathawalla U-K, Silverstein P, & Syed M (2020). Easing Into Open Science: A Tutorial for Graduate Students [Preprint]. PsyArXiv. 10.31234/osf.io/vzjdp · DOI
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Resting brain activity in early childhood predicts IQ at 18 years
Tan, E., Tang, A., Debnath, R., Humphreys, K. L., Zeanah, C. H., Nelson, C. A., & Fox, N. A. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101287 · Cited by 22 · see citing works
- Authors
- Donoghue, T., Schaworonkow, N., & Voytek, B.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Donoghue, Thomas
- Venue
- European Journal of Neuroscience
- Publisher
- Wiley
- Published
- 2021-07-16
- Volume
- 55
- Issue
- 11-12
- Pages
- 3502-3527
- ISSN
- 0953-816X (ISSN-L)
- 1460-9568
- PMC
- PMC8761223
- OpenAlex
- W3182944339
- Cited by
- 309
- References
- 165
- Citations by year
- 2026: 52 · 2025: 73 · 2024: 62 · 2023: 59 · 2022: 46 · 2021: 15 · 2017: 1 · 2012: 1
- FWCI
- 23.94
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- European Journal of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- PubMed Central (acceptedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Funders
- National Science Foundation (BCS‐1736028)
- National Institute of General Medical Sciences (R01GM134363‐02)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- neural oscillations, oscillation detection, aperiodic activity, temporal variation, wave shape, signal-to-noise ratio
- MeSH terms
- Cognition, Computer Simulation
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