Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations
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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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Sparsity is often assumed to avoid over-fitting in inverse problems and has increasing applications in signal and image processing.
“Sparsity is often assumed to avoid over-fitting in inverse problems, and has increasing applications in signal and image processing (43, 50–54).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [53]
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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.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-04
- Volume
- 70
- Pages
- 410-422
- ISSN
- 1053-8119 (ISSN-L)
- 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
- 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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