Minimum norm estimation is a mathematical approach used in neuroimaging to localize brain activity by estimating the source of electrical or magnetic signals recorded from the scalp. It operates on the principle of finding the most spatially concentrated source distribution that explains the observed data while minimizing the overall energy of the estimated sources. This technique is particularly valuable for its ability to provide a unique solution when dealing with underdetermined inverse problems, like those commonly encountered in magnetoencephalography.
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