Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations

By M. Gerster, G. Waterstraat, Vladimir Litvak, Klaus Lehnertz, Alfons Schnitzler, E. Florin, Gabriel Curio, Vadim V. Nikulin. 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. Q. Yin
2. Jae-Hwan Kang
3. E. Johnson
4. Young-Ju Jeon
5. Jang-Han Bae
6. N. Ofen

In Signal Processing

1. Jae-Hwan Kang
2. Young-Ju Jeon
3. Jang-Han Bae

Citations in the corpus

• Scale-Free Neurodynamics as Functional Fingerprint of Brain Regions (2026, unknown): “It is also important to note that several known factors—such as recording conditions, age, medication, arousal state, and even hormonal cycle in women—could influence the 1/ f slope [13,17,20].”
• Non-Neural Sources Systematically Impact Aperiodic EEG Activity (2026, unknown): “As pointed out by a previous study 36 , oscillatory peaks near the lower or upper bound of the fitted frequency ranges can bias the estimation of the aperiodic parameters in the algorithm of specparam 1 , but also in an alternative estimation method such as irregular-resampling autospectral…”
• Hemispherotomy leads to persistent sleep-like slow waves in the isolated cortex of awake humans (2025, unknown): “The neurophysiological 1/f-like distribution originates from aperiodic and quasi-periodic activity [81,82].”
• Model selection for spectral parameterization (2024, unknown): “Secondly, the improved accuracy of aperiodic component estimates is critical for characterizing complex neural dynamics (Donoghue et al., 2020; Gerster et al., 2022).”
• Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review (2024, background): “In this case, it means the flatness of the 1/f noise in the PSD of a single EEG signal, which is mainly measured as a decline in the aperiodic exponent [76,82].”
• Neurophysiological mechanisms of cognition in the developing brain: Insights from intracranial EEG studies (2023, background): “The exponent describes the flatness/steepness of the power spectrum and reflects synaptic excitation and inhibition (E:I) balance, with a steeper power spectrum associated with a reduced E:I ratio (Gao et al., 2017; Gerster et al., 2022).”
• Globally elevated excitation–inhibition ratio in children with autism spectrum disorder and below-average intelligence (2021, methodology): “Following previous studies (23, 25, 28), we sought to estimate the slope of aperiodic activity at the high-frequency part of the spectrum.”
• Linking the multiple-demand cognitive control system to human electrophysiological activity (2025, unknown): “Both approaches yield robust and comparable estimations in general, though they each have their advantages and limitations (Cross et al., 2022; Gerster et al., 2022).”