EEG Extended Source Imaging with Variation Sparsity and \(L_p\) -Norm Constraint
摘要
Accurately reconstructing the location and extent of cortical sources is crucial for cognitive research and clinical applications. Regularization methods that use the $$L_1$$ -norm in the spatial variation domain effectively estimate cortical extended sources. However, in the variation domain, employing $$L_1$$ -norm constraint tends to overestimate the extent of sources. Hence, to achieve more precise estimations of both the location and extent of sources, further sparseness-enforced regularizations are required. In this work, we develop a robust EEG source imaging method, VSSI- $$L_p$$ , to estimate extended cortical sources. VSSI- $$L_p$$ employs the $$L_p$$ -norm ( $$0