ArticleThe European journal of neuroscience2022
Robust estimation of 1/f activity improves oscillatory burst detection.
Article in The European journal of neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Aperiodic and Hurst EEG exponents across early human brain development: A systematic review.Developmental cognitive neuroscience · 2024Pooled it
- sBOSC: A Method for Source-Level Identification of Neural Oscillations in Electromagnetic Brain Signals.Psychophysiology · 2026Article
- Brain-computer interface-based neurofeedback training enables transferable control of cortical state switching in humans.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Reduced inhibition, bursting, and accelerated oscillations drive early hippocampal hyperactivity in Alzheimer's disease in vivo.Communications biology · 2026Article
- How to Improve the Reliability of Aperiodic Parameter Estimates in M/EEG: A Method Comparison.Psychophysiology · 2026Article
- pBOSC: A method for source-level identification of neural oscillations in electromagnetic brain signals.bioRxiv : the preprint server for biology · 2025Article
- How to Improve the Reliability of Aperiodic Parameter Estimates in M/EEG: A Method Comparison and Recommendations for Best Practices.bioRxiv : the preprint server for biology · 2025Article
- Article
- Differential representations of spatial location by aperiodic and alpha oscillatory activity in working memory.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Discourse Focus and Memory Encoding: The Role of Trial-Level Alpha Power.bioRxiv : the preprint server for biology · 2025Article
- BSD: A Bayesian Framework for Parametric Models of Neural Spectra.The European journal of neuroscience · 2025Article
- High-power transient 12-30 Hz beta event features as early biomarkers of Alzheimer's disease conversion: An MEG study.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Investigating mu and alpha oscillations as indicators of intra-individual success and inter-individual ability in motor imagery performance.Frontiers in psychology · 2025Article
- Model selection for spectral parameterization.bioRxiv : the preprint server for biology · 2024Article
- Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury.bioRxiv : the preprint server for biology · 2024Article
- The role of alpha oscillations in free- and goal-directed semantic associations.Human brain mapping · 2024Article
- Modulation of hippocampal theta oscillations via deep brain stimulation of the parietal cortex depends on cognitive state.Cortex; a journal devoted to the study of the nervous system and behavior · 2024Article
- Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes.bioRxiv : the preprint server for biology · 2023Article
- Genuine beta bursts in human working memory: controlling for the influence of lower-frequency rhythms.bioRxiv : the preprint server for biology · 2023Article
- Approaches to characterizing oscillatory burst detection algorithms for electrophysiological recordings.Journal of neuroscience methods · 2023Article
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Abstract
Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain-behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC, which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non-linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a 'knee' below ~.5-10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g., theta- and alpha-bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta-band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials.
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