Interpreting magnetic fields of the brain: minimum norm estimates

By Matti S. Hämäläinen, Risto J. Ilmoniemi. Medical and Biological Engineering and Computing, 2006

1,929 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ef162bbfe2bbb9e928a59471c4239c3c1026b352

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Citations in the corpus

Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals (2011, neutral): “, the “simplest” solution) is sought [26].”
Brainstorm: A User-Friendly Application for MEG/EEG Analysis (2011, methodology): “In Brainstorm, the main method to estimate source activities is adapted from the depth-weighted minimum L2 norm estimator of cortical current density [42], which can subsequently be normalized using either the statistics of noise (dSPM [43]) or the data covariance (sLORETA [44]), as estimated”
The Effect of Imagination on Stimulation: The Functional Specificity of Efference Copies in Speech Processing (2013, background): “BecauseMEG is sensitive to electromagnetic fields generated from current sources that are in sulci and tangential to the cortical surface (Hämäläinen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993), deeper sources are given more weight to overcome the MNE bias toward superficial currents and current ”
Penalized Least Squares Regression Methods and Applications to Neuroimaging (2010, neutral): “We refer the reader to Scherg and Voncramon (1986); Mosher and Leahy (1998); Uutela et al. (1999); Hämäläinen and Ilmoniemi (1994); Silvaa et al. (2004); Valdés-Sosa et al. (2009) and Pascual-Marqui et al. (1994); Pascual-Marqui (2002) among many others.”
MEG and EEG data analysis with MNE-Python (2013, neutral): “The most popular of these is the software namesake MNE, which stands for Minimum-Norm Estimate (Wang et al., 1992; Hämäläinen and Ilmoniemi, 1994), and its variants which include dSPM (Dale et al., 2000) and sLORETA (Pascual-Marqui, 2002).”
MNE software for processing MEG and EEG data (2013, methodology): “One popular source prior is exempli ﬁ ed in the minimum-norm estimates (MNE, Hämäläinen and Ilmoniemi, 1994; Wang et al., 1992).”
Recurrence is required to capture the representational dynamics of the human visual system (2019, methodology): “Source reconstructions were performed using MNE (18), as implemented in the MNE Python toolbox (49).”
Hierarchical Bayesian estimation for MEG inverse problem (2004, methodology): “For example, the minimum norm solution (Hamalainen and Ilmoniemi, 1994) can be derived if AE 0 is the N-by-N identity matrix.”
Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients (2012, neutral): “…trials using the minimum norm estimate with smoothness prior, a method that has the advantage of requiring no a priori assumption about the nature of the source distribution (Hamalainen and Ilmoniemi, 1994) and of providing stable solutions also in the presence of noise (Silva et al., 2004).”
A critical assessment of connectivity measures for EEG data: A simulation study (2013, background): “Theweightedminimum-norm(WMN) source estimate (Hämäläinen and Ilmoniemi, 1994; Ioannides et al., 1990; Jeffs et al., 1987) is the source distribution with minimal power that explains the EEGmeasurement.”
FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data (2010, methodology): “…are available for the estimation of the location and strength of neuronal activity, including dipole ﬁtting based on nonlinear optimization, [2] scanning methods such as minimum variance beamformers in the time and frequency domain [4, 5], and linear estimation of distributed source models [3].”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “Instead of decoding channel level activations, source reconstruction (e.g., beamformer [Van Veen, Van Drongelen, Yuchtman, & Suzuki, 1997] or minimum norm estimate [Hämäläinen & Ilmoniemi, 1994]) can be applied during preprocessing.”
EEG and MEG Data Analysis in SPM8 (2011, methodology): “, it can reproduce a variety of standard constraints of the sort associated with minimum norm [29], LORETA [30], and other well-known solutions to the inverse problem).”
The time course of visual word recognition as revealed by linear regression analysis of ERP data (2006, methodology): “This would be the case, for example, for linear estimation techniques often employed in distributed source analyses of EEG and MEG data (Hämäläinen and Ilmoniemi, 1994; Grave de Peralta Menendez et al., 1997; Hauk, 2004).”