Performance of cortical LORETA and cortical CLARA applied to MEG data

LORETA and Classical LORETA Analysis Recursively Applied (CLARA) have previously been used in brain volume space. Applying a surface Laplacian operator enables to apply these approaches also on the cortical surface. Here, the performance of these methods using the cortical source space constraint is investigated, when applying them to simulated and real MEG data. The comparison of Cortical LORETA and Cortical CLARA shows that Cortical CLARA yields more focal results with superior localization precision and better depth estimation.