Abstract:
Electromagnetic brain imaging reconstructs brain activity from non-invasive magnetic fields and electric potentials. This imaging modality struggles to estimate source number, location, and time course, especially for distributed brain sources with complex spatial extent.
We present a robust empirical Bayesian algorithm that improves distributed brain source activity reconstruction using kernel smoothing and hyperparameter tiling. Smooth Champagne is the proposed sparse source reconstruction algorithm that builds on Champagne’s performance features.
Smooth Champagne resists noise, interference, and highly correlated brain source activity. Smooth Champagne outperforms benchmark algorithms in determining the spatial extent of distributed source activity in simulations. Smooth Champagne faithfully reconstructs MEG and EEG data.
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