A Bayesian Framework for Unifying Data Cleaning, Source Separation and Imaging of Electroencephalographic Signals

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Abstract Electroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms for data cleaning, source separation, and localization. Typically, these problems are addressed by independent heuristics, limiting the use of EEG images on a variety of applications. Here, we propose a unifying parametric empirical Bayes framework in which these dissimilar problems can be solved using a single algorithm (PEB+). We use sparsity constraints to adaptively segregate brain sources into maximally independent components with known anatomical support, while minimally overlapping artifactual activity. Of theoretical relevance, we demonstrate the connections between Infomax ICA and our framework. On real data, we show that PEB+ outperforms Infomax for source separation on short time-scales and, unlike the popular ASR algorithm, it can reduce artifacts without significantly distorting clean epochs. Finally, we analyze mobile brain/body imaging data to characterize the brain dynamics supporting heading computation during full-body rotations, replicating the main findings of previous experimental literature.
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