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Electroencephalography (EEG) signal has been playing a crucial role in clinical diagnosis and treatment of neurological diseases. However, it is very challenging to efficiently reconstruct the high-resolution brain image from very few scalp EEG measurements due to high ill-posedness. Recently some efforts have been devoted to developing EEG source reconstruction methods using various forms of regularization,...
EEG source imaging is able to reconstruct sources in the brain from scalp measurements with high temporal resolution. Due to the limited number of sensors, it is very challenging to locate the source accurately with high spatial resolution. Recently, several total variation (TV) based methods have been proposed to explore sparsity of the source spatial gradients, which is based on the assumption that...
Compressive sensing (CS) has great potential to reduce imaging time. It samples very few linear projections, and exploits sparsity or compressibility to reconstruct images from the measurements. Medical and most natural images usually contain various fine features, details and textures. Widely used total variation (TV) and wavelet sparsity are not so effective in reconstructing these images. We propose...
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