The results of the fMRI analysis were used to impose plausible constraints on the MEG inverse calculation using a "weighted" minimum-norm approach to improve spatial resolution of the spatiotemporal activity estimates.
The standardized low resolution brain electromagnetic tomography Minimum Norm Least Squares approach (Pascual-Marqui 2002) was used for distributed source modeling.
Source-level analysis was performed using signals from all 306 sensors with the L2 minimum norm estimate (MNE) approach (Hamalainen and Sarvas 1989; Ilmoniemi 1993; Hämäläinen and Ilmoniemi 1994).
Experimental results show the superiority of this approach over minimum norm solutions.
We propose two more general KM-CQ algorithms with the generalized variable stepsize (1.9) or (1.10), and they can be used to approach the minimum norm solution of the SFP that solves special variational inequalities.
As a special case, the minimum norm solution of the SFP can be approached iteratively.
As a special case, the minimum norm solution of the split feasibility problem can be approached iteratively.
Some principled techniques used to estimate these parameters include minimum norm quadratic unbiased estimation (Rao 1971), the variance least-squares approach (Amemiya 1977; Demidenko 2004, p. 223), and the Bayesian approaches implemented in the R package BGLR (Rodrıguez and De Los Campos 2012).
Occam's approach is an algorithm for generating a smooth minimum norm model to an appropriate fit of the data.
