Improving EEG Source Localization through Spatio-temporal Sparse Bayesian Learning
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Sparse Bayesian Learning (SBL) approaches to the EEG inverse problem such as Champagne have been shown to outperform traditional l1-norm based methods in terms of reconstructing sparse source configurations. Current approaches are however sensitive to strong noise contributions and assume independent samples, whereas the neurophysiological time series are strongly auto-correlated. Here we present extensions, backed by compressive sensing theory, to the Champagne algorithm that improve the reconstruction performance in low-SNR settings as well as in the presence of correlated measurements. Our numerical simulations using a realistic EEG forward model confirm the efficacy of our approaches.
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