Time-frequency (TF) analysis of M/EEG data enables rich understanding of cortical dynamics underlying cognition, health, and disease. There are many algorithms for time-frequency decomposition of M/EEG neural data, but they are implemented in an inconsistent manner and most existing toolboxes either 1) contain only one or a few transforms, or 2) are not adapted to analyze multichannel, multitrial M/EEG data. This makes entry into time-frequency daunting for new practitioners and limits the ability of the community to flexibly compare the performance of multiple TF methods on M/EEG data. This paper introduces the toolbox for MATLAB, which includes multiple TF transformation algorithms that are implemented in a consistent fashion and produce consistent output. The toolbox includes TF decomposition algorithms of both linear and quadratic classes, utilities for resampling, averaging, and baseline correction of TF representations, and tools for visualizing and interacting with single-trial or averaged TF representations over multiple channels. This paper introduces these utilities, and applies them to synthetic and EEG data to demonstrate the toolbox.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635047 | PMC |
| http://dx.doi.org/10.1101/2023.11.01.565154 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology.
Proc Natl Acad Sci U S A
January 2025
Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130.
Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions.
View Article and Find Full Text PDFAttentional Inhibition Ability Predicts Neural Representation During Challenging Auditory Streaming.
Cogn Affect Behav Neurosci
January 2025
Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France.
Focusing on a single source within a complex auditory scene is challenging. M/EEG-based auditory attention detection (AAD) allows to detect which stream an individual is attending to within a set of multiple concurrent streams. The high interindividual variability in the auditory attention detection performance often is attributed to physiological factors and signal-to-noise ratio of neural data.
View Article and Find Full Text PDFM/EEG source localization for both subcortical and cortical sources using a convolutional neural network with a realistic head conductivity model.
APL Bioeng
December 2024
Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan.
While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established noninvasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Electrophysiological source imaging (ESI) addresses this by noninvasively exploring the neuronal origins of M/EEG signals. Although subcortical structures are crucial to many brain functions and neuronal diseases, accurately localizing subcortical sources of M/EEG remains particularly challenging, and the feasibility is still a subject of debate.
View Article and Find Full Text PDFThe impact of ROI extraction method for MEG connectivity estimation: practical recommendations for the study of resting state data.
Neuroimage
October 2023
Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada.
Magnetoencephalography and electroencephalography (M/EEG) seed-based connectivity analysis requires the extraction of measures from regions of interest (ROI). M/EEG ROI-derived source activity can be treated in different ways. It is possible, for instance, to average each ROI's time series prior to calculating connectivity measures.
View Article and Find Full Text PDFDifferential neural mechanisms underlie cortical gating of visual spatial attention mediated by alpha-band oscillations.
Proc Natl Acad Sci U S A
November 2024
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544.
Selective attention relies on neural mechanisms that facilitate processing of behaviorally relevant sensory information while suppressing irrelevant information, consistently linked to alpha-band oscillations in human M/EEG studies. We analyzed cortical alpha responses from intracranial electrodes implanted in eight epilepsy patients, who performed a visual spatial attention task. Electrocorticographic data revealed a spatiotemporal dissociation between attention-modulated alpha desynchronization, associated with the enhancement of sensory processing, and alpha synchronization, associated with the suppression of sensory processing, during the cue-target interval.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!