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

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 temp...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Fla.), 2013-04, Vol.70, p.410-422
Hauptverfasser: Gramfort, A., Strohmeier, D., Haueisen, J., Hämäläinen, M.S., Kowalski, M.
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Sprache:eng
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Zusammenfassung: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. ► Our method solves the M/EEG inverse problem without assuming source stationarity. ► We localize sources in space, time and frequency in one step. ► We provide results on simulations and two publicly available MEG datasets. ► We use short time Fourier transforms (STFT) and modern optimization techniques. ► The solver is tractable and fast on real data.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2012.12.051