Magneto-/Electroencephalography with Space-Time Sparse Priors

Authors:
Andrew Bolstad;Barry Van Veen;Robert Nowak
Affiliations:
University of Wisconsin, Department of Electrical and Computer Engineering, Madison, WI 53706;University of Wisconsin, Department of Electrical and Computer Engineering, Madison, WI 53706;University of Wisconsin, Department of Electrical and Computer Engineering, Madison, WI 53706
Venue:
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Year:
2007

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Sparse reconstruction by separable approximation

IEEE Transactions on Signal Processing

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Abstract

We examine the performance of "Space-Time Sparsity" (STS) penalized reconstruction of brain activity from magneto-/electroencephalographic (MEG/EEG) recordings. We propose two STS priors, both of which favor activation of a few localized areas of cortex over a limited time duration and bandwidth via appropriate basis functions. This provides a reasonable model of true brain activity which minimizes the impact of the inherently ill-conditioned, low SNR, spatial inverse problem. We use an expectation-maximization (EM) algorithm to solve the STS penalized least-squares cost function. The solution localizes brain activity in space and time, providing support for a refined signal estimate (e.g. minimum norm least-squares). We illustrate the approach on both simulated and real data and provide preliminary theoretical analysis of performance.