Cited by 234EEG and Brain-Computer InterfacesNumerical methods in inverse problemsDirection-of-Arrival Estimation Techniques

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Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations

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Gramfort, A., Strohmeier, D., Haueisen, J., Hämäläinen, M.S., Kowalski, M.·2013·NeuroImage

DOI 10.1016/j.neuroimage.2012.12.051PMC PMC3615257

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Abstract

Magnetoencephalography (MEG) and electroencephalography (EEG) allow functional brain imaging with high temporal resolution. While solving the inverse problem independently at every time point can give an image of the active brain at every millisecond, such a procedure does not capitalize on the temporal dynamics of the signal. Linear inverse methods (minimum-norm, dSPM, sLORETA, beamformers) typically assume that the signal is stationary: regularization parameter and data covariance are independent of time and the time varying signal-to-noise ratio (SNR). Other recently proposed non-linear inverse solvers promoting focal activations estimate the sources in both space and time while also assuming stationary sources during a time interval. However such a hypothesis holds only for short time intervals. To overcome this limitation, we propose time-frequency mixed-norm estimates (TF-MxNE), which use time-frequency analysis to regularize the ill-posed inverse problem. This method makes use of structured sparse priors defined in the time-frequency domain, offering more accurate estimates by capturing the non-stationary and transient nature of brain signals. State-of-the-art convex optimization procedures based on proximal operators are employed, allowing the derivation of a fast estimation algorithm. The accuracy of the TF-MxNE is compared with recently proposed inverse solvers with help of simulations and by analyzing publicly available MEG datasets.

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Authors: Gramfort, A., Strohmeier, D., Haueisen, J., Hämäläinen, M. S., & Kowalski, M.
Affiliations: Centre National de la Recherche Scientifique (FR)
Technische Universität Ilmenau (DE)
Harvard University (US)
Supélec (FR)
Corresponding author: Gramfort, A.
ORCID: Gramfort, A.
Strohmeier, D.
Haueisen, J.
Hämäläinen, M.S.
Kowalski, M.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2013-04
Volume: 70
Pages: 410-422
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2012.12.051
PMC: PMC3615257
OpenAlex: W2090530235
Cited by: 234
References: 49
Citations by year: 2026: 3 · 2025: 9 · 2024: 14 · 2023: 19 · 2022: 20 · 2021: 17 · 2020: 19 · 2019: 30 · 2018: 25 · 2017: 16 · 2016: 17 · 2015: 19 · 2014: 15 · 2013: 11
FWCI: 11.34
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
HAL (submittedVersion, other-oa)
Funders: National Center for Research Resources (P41 RR014075-11)
National Institute of Biomedical Imaging and Bioengineering (5R01EB009048, R01 EB006385)
German Research Foundation (Ha 2899/8-2)
French Agence Nationale de la Recherche (ANR) (ViMAGINEANR-08-BLAN-0250-02)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
Keywords: sparse priors, convex optimization, proximal operator, ill-posed inverse problems, MEG, EEG
MeSH terms: Electroencephalography, Humans, Models, Neurological, Time Factors, Magnetoencephalography

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Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations

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