Methodological Considerations for Studying Neural Oscillations

By Thomas Donoghue, N. Schaworonkow, Bradley Voytek. 2021

Subfields: Cognitive Neuroscience, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. H. Gudi-Mindermann
2. R. Kekunnaya
3. Vladimir Litvak
4. D. Melcher
5. Klaus Lehnertz
6. F. Bigelow
7. L. Stange
8. D. Bottari

In Signal Processing

1. Vladimir Litvak
2. Vadim V. Nikulin
3. Gabriel Curio

Citations in the corpus

• The Interaction between Sleep and Development on Wake EEG Oscillations (2026, unknown): “As explained by previous papers, changes in spectral power do not differentiate between changes in oscillation amplitudes or densities (Quinn et al., 2019; Tal et al., 2020; Zich et al., 2020; Donoghue et al., 2022; Snipes et al., 2023).”
• Alterations in aperiodic and periodic EEG activity in young children with Down syndrome (2024, unknown): “…power, we separately analyzed aperiodic activity (reflecting broadband background signal) and periodic activity (reflecting narrowband oscillatory components) allowing for a more accurate characterization of group differences in the power spectra (Ostlund et al., 2022; Donoghue et al., 2021).”
• Alterations in aperiodic and periodic EEG activity in young children with Down syndrome (2024, unknown): “…power, we separately analyzed aperiodic activity (reflecting broadband background signal) and periodic activity (reflecting narrowband oscillatory components) allowing for a more accurate characterization of group differences in the power spectra (Ostlund et al., 2022; Donoghue et al., 2021).”
• Resting brain activity in early childhood predicts IQ at 18 years (2023, unknown): “, the arrhythmic component of neural oscillations) (Cellier et al., 2021; Donoghue et al., 2022).”
• Periodic and Aperiodic EEG Features as Potential Markers of Developmental Dyslexia (2023, background): “When studying neural oscillations, ignoring the influence of aperiodic activity on the periodic one might lead to misinterpretations of power and/or frequency modulations which could be induced by offset/exponent changes [28,30].”
• The development of oscillatory and aperiodic resting state activity is linked to a sensitive period in humans (2023, background): “Thus, it is nowadays considered as essential to remove the aperiodic components of the power spectrum to identify oscillatory activity in distinct narrow frequency bands (Pesaran, 2018; Donoghue et al., 2021).”
• Time-resolved parameterization of aperiodic and periodic brain activity (2022, unknown): “We emphasize that the periodic features of the recordings were non-sinusoidal and therefore were not explored further with the methods discussed herein (Donoghue et al., 2021; Figure 5 – figure supplement 1).”
• Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood (2021, background): “Equally importantly, it further allows explicit measurement of the aperiodic signal, which is likely to be driven by a unique set of neural generators (Donoghue et al., 2021; Ostlund et al., 2021b).”
• Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations (2021, background): “For that reason, estimating the 1/f component before determining the power of periodic activity has recently been suggested (Donoghue et al., 2021; Wen & Liu, 2016).”
• Stability of spectral estimates in resting-state magnetoencephalography: Recommendations for minimal data duration with neuroanatomical specificity (2021, unknown): “measurement of oscillatory components of neural activity (Donoghue et al., 2021).”