ArticleJournal of neuroscience methods2023
Approaches to characterizing oscillatory burst detection algorithms for electrophysiological recordings.
Article in Journal of neuroscience methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Manifolds in the Medial Premotor Cortex During Switching From Attending to Tapping to a Metronome.Annals of the New York Academy of Sciences · 2026Article
- Manifold properties in the macaque medial premotor cortex during switching from attending to tapping to a metronome.bioRxiv : the preprint server for biology · 2025Article
- Microcircuitry and oscillation features across model cortical regions.The ... Midwest Symposium on Circuits and Systems conference proceedings : MWSCAS. Midwest Symposium on Circuits and Systems · 2025Article
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7 authors.
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Abstract
backgroundCognitive processes are associated with fast oscillations of the local field potential and electroencephalogram. There is a growing interest in targeting them because these are disrupted by aging and disease. This has proven challenging because they often occur as short-lasting bursts. Moreover, they are obscured by broad-band aperiodic activity reflecting other neural processes. These attributes have made it exceedingly difficult to develop analytical tools for estimating the reliability of detection methods. NEW
methodTo address this challenge, we developed an open-source toolkit with four processing steps, that can be tailored to specific brain states and individuals. First, the power spectrum is decomposed into periodic and aperiodic components, each of whose properties are estimated. Second, the properties of the transient oscillatory bursts that contribute to the periodic component are derived and optimized to account for contamination from the aperiodic component. Third, using the burst properties and aperiodic power spectrum, surrogate neural signals are synthesized that match the observed signal's spectrotemporal properties. Lastly, oscillatory burst detection algorithms run on the surrogate signals are subjected to a receiver operating characteristic analysis, providing insight into their performance.
resultsThe characterization algorithm extracted features of oscillatory bursts across multiple frequency bands and brain regions, allowing for recording-specific evaluation of detection performance. For our dataset, the optimal detection threshold for gamma bursts was found to be lower than the one commonly used. COMPARISON WITH EXISTING
methodsExisting methods characterize the power spectrum, while ours evaluates the detection of oscillatory bursts.
conclusionsThis pipeline facilitates the evaluation of thresholds for detection algorithms from individual recordings.
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