Sentence examples for minimum norm estimation from inspiring English sources

Minimum norm estimation provides chronometric and localization data showing that regions known to be involved in the mentalizing, "mirror neuron" and social perception networks have simultaneous activations between 100 and 700 ms post-stimulus, a period which may be thought as corresponding to early stages of social processes.

A major advantage of beamformer analysis relative to alternative source localisation techniques such as equivalent current dipole modelling or minimum norm estimation (which take evoked-average data as input) is the ability to image changes in cortical oscillatory power that do not give rise to a strong signal in the evoked-average response [11].

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].

We use distributed minimum-norm estimation (MNE) to determine the main neuronal sources of the effects of the predictor variables.

The multiple-channel recording gave the possibility to determine the loci of the evoked neuronal activity, using dipole modeling (Scherg 1992) or minimum-norm estimation (Hämäläinen and Ilmoniemi 1994).

For both affective conditions, the correlation with the minimum-norm estimation of the condition across 228 to 245 ms reached significance with the depression score (pleasant condition: r = .40; unpleasant condition: r = .37, p < .05), but not with the shutdown dissociation strength or PTSD symptom severity (all ps > .2).2

Minimum-norm estimation makes minimal assumptions about the structure of the brain generators (e.g., their number or locations) and is optimal for the analysis of complex or completely unknown source configurations within the general resolution limits of MEG measurements (Hämäläinen and Ilmoniemi 1994; Hauk and Pulvermuller 2004, Hauk et al. 2011).

The underlying neural sources were estimated by means of the least square minimum-norm-estimation (L2-MNE) approach.

To evaluate the underlying neural activity, source-space activity was estimated for each time point in each condition and subject, using the least square minimum-norm-estimation (L2-MNE) method [54].

High-resolution structural MRI was obtained in each subject, to define the shells for a boundary element forward solution and to reconstruct the cortex providing the solution space for a noise-normalized minimum norm source estimation procedure.