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arXiv · 2112.12178

Electromagnetic neural source imaging under sparsity constraints with SURE-based hyperparameter tuning

Pierre-Antoine Bannier·Quentin Bertrand·Joseph Salmon·Alexandre Gramfort
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Abstract

Estimators based on non-convex sparsity-promoting penalties were shown to yield state-of-the-art solutions to the magneto-/electroencephalography (M/EEG) brain source localization problem. In this paper we tackle the model selection problem of these estimators: we propose to use a proxy of the Stein's Unbiased Risk Estimator (SURE) to automatically select their regularization parameters. The effectiveness of the method is demonstrated on realistic simulations and $30$ subjects from the Cam-CAN dataset. To our knowledge, this is the first time that sparsity promoting estimators are automatically calibrated at such a scale. Results show that the proposed SURE approach outperforms cross-validation strategies and state-of-the-art Bayesian statistics methods both computationally and statistically.

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Pierre-Antoine Bannier, Quentin Bertrand, Joseph Salmon, Alexandre Gramfort. 2021-10-28. Electromagnetic neural source imaging under sparsity constraints with SURE-based hyperparameter tuning. https://arxiv.org/abs/2112.12178

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