ArticleJournal of neurophysiology2021
Automatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes.
Article in Journal of neurophysiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Rhythmic stimulation elicits multiple, concurrent neural responses.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Whisker stimulation reinforces a resting-state network in the barrel cortex: Nested oscillations and avalanches.PLoS computational biology · 2026Article
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.Journal of affective disorders · 2025Article
- Applying Entropic Measures, Spectral Analysis, and EMD to Quantify Ion Channel Recordings: New Insights into Quercetin and Calcium Activation of BK Channels.Entropy (Basel, Switzerland) · 2025Article
- Multi-scale parameterization of neural rhythmicity with lagged Hilbert autocoherence.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Preparatory activity of anterior insula predicts conflict errors: integrating convolutional neural networks and neural mass models.Scientific reports · 2024Article
- Respiratory Inductance Plethysmography to Assess Fatigability during Repetitive Work.Sensors (Basel, Switzerland) · 2022Article
- Detection and analysis of cortical beta bursts in developmental EEG data.Developmental cognitive neuroscience · 2022Article
- Understanding Harmonic Structures Through Instantaneous Frequency.IEEE open journal of signal processing · 2022Article
- Article
- An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces.Frontiers in neuroscience · 2021Article
- Diazepam alters the shape of alpha oscillations recorded from human cortex using EEG.Imaging neuroscience (Cambridge, Mass.)Article
- A neuroscientist's guide to neural burst detection.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Neurophysiological signals are often noisy, nonsinusoidal, and consist of transient bursts. Extraction and analysis of oscillatory features (such as waveform shape and cross-frequency coupling) in such data sets remains difficult. This limits our understanding of brain dynamics and its functional importance. Here, we develop iterated masking empirical mode decomposition (itEMD), a method designed to decompose noisy and transient single-channel data into relevant oscillatory modes in a flexible, fully data-driven way without the need for manual tuning. Based on empirical mode decomposition (EMD), this technique can extract single-cycle waveform dynamics through phase-aligned instantaneous frequency. We test our method by extensive simulations across different noise, sparsity, and nonsinusoidality conditions. We find itEMD significantly improves the separation of data into distinct nonsinusoidal oscillatory components and robustly reproduces waveform shape across a wide range of relevant parameters. We further validate the technique on multimodal, multispecies electrophysiological data. Our itEMD extracts known rat hippocampal θ waveform asymmetry and identifies subject-specific human occipital α without any prior assumptions about the frequencies contained in the signal. Notably, it does so with significantly less mode mixing compared with existing EMD-based methods. By reducing mode mixing and simplifying interpretation of EMD results, itEMD will enable new analyses into functional roles of neural signals in behavior and disease.
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