Sentence examples for minimum norm estimate from inspiring English sources

EEG analyses used the minimum norm estimate (MNE -Python MNE -Python suite57

Several regularization functions have been proposed in the EEG community: Hämäläinen and Ilmoniemi in [7] proposed a squared Frobenius norm penalty ( ∥ S ∥ F 2 ), which they named Minimum Norm Estimate (MNE).

Whereas the previous studies were in principle based on localizing single point sources in the brain that can explain the measured magnetic fields, the present study applied a distributed source model, the cortically-based minimum norm estimate, which is well suited to analyze sources in an extensive network of brain areas that are activated more or less simultaneously [70].

MEM was compared with minimum norm estimate, dynamic statistical parametric mapping, and standardized low-resolution electromagnetic tomography.

Activity in the source space was determined by the minimum norm estimate including 197 dipoles located on a sphere [ 21].

Here, we estimate the location of these sources using the neuroanatomically constrained minimum norm estimate (MNE) procedure, based on distributed source modeling rather than equivalent current dipoles (Hämäläinen et al. 1993).

Hämäläinen, M. S. & Ilmoniemi, R. J. Interpreting magnetic fields of the brain: minimum norm estimates.

Unconventional anatomically constrained signed minimum norm estimates of MEG data were most sensitive to the primary experimental manipulation, suggesting that the conventional unsigned unconstrained method is sub-optimal for studying written word processing.

To define the constrain, we can use mathematical restrictions (minimum norm estimates) or anatomical, physiological, and functional prior information.

We recorded the resting-state magnetoencephalographic activities of 28 patients with FM and 28 age- and sex-matched controls, and analyzed the source-based functional connectivity between the insula and the DMN at 1 40 Hz by using the minimum norm estimates and imaginary coherence methods.

We analyzed resting-state functional connectivity in two stages: In the first part, the resting-state MEG recording of each participant was analyzed using depth-weighted minimum norm estimates to obtain the distributed and dynamic cortical source model [40], which presented each cortical vertex as a current dipole and included ~15,000 vertices in the forward model.