scholarly journals Comparing EEG/MEG neuroimaging methods based on localization error, false positive activity, and false positive connectivity

2018 ◽  
Author(s):  
Roberto D. Pascual-Marqui ◽  
Pascal Faber ◽  
Toshihiko Kinoshita ◽  
Kieko Kochi ◽  
Patricia Milz ◽  
...  
Keyword(s):  
False Positive ◽  
Statistical Parametric Mapping ◽  
Minimum Variance ◽  
Point Sources ◽  
Imaging Method ◽  
Localization Error ◽  
Pascal Program ◽  
Linearly Constrained ◽  
Data Files ◽  
Theoretical Population

1.AbstractEEG/MEG neuroimaging consists of estimating the cortical distribution of time varying signals of electric neuronal activity, for the study of functional localization and connectivity. Currently, many different imaging methods are being used, with very different capabilities of correct localization of activity and of correct localization of connectivity. The aim here is to provide a guideline for choosing the best (i.e. least bad) imaging method. This first study is limited to the comparison of the following methods for EEG signals: sLORETA and eLORETA (standardized and exact low resolution electromagnetic tomography), MNE (minimum norm estimate), dSPM (dynamic statistical parametric mapping), and LCMVBs (linearly constrained minimum variance beamformers). These methods are linear, except for the LCMVBs that make use of the quadratic EEG covariances. To achieve a fair comparison, it is assumed here that the generators are independent and widely distributed (i.e. not few in number), giving a well-defined theoretical population EEG covariance matrix for use with the LCMVBs. Measures of localization error, false positive activity, and false positive connectivity are defined and computed under ideal no-noise conditions. It is empirically shown with extensive simulations that: (1) MNE, dSPM, and all LCMVBs are in general incapable of correct localization, while sLORETA and eLORETA have exact (zero-error) localization; (2) the brain volume with false positive activity is significantly larger for MN, dSPM, and all LCMVBs, as compared to sLORETA and eLORETA; and (3) the number of false positive connections is significantly larger for MN, dSPM, all LCMVBs, and sLORETA, as compared to eLORETA. Non-vague and fully detailed equations are given. PASCAL program codes and data files are available. It is noted that the results reported here do not apply to the LCMVBs based on EEG covariance matrices generated from extremely few generators, such as only one or two independent point sources.

scholarly journals Comparison of beamformer implementations for MEG source localization

2019 ◽  
Author(s):  
Amit Jaiswal ◽  
Jukka Nenonen ◽  
Matti Stenroos ◽  
Alexandre Gramfort ◽  
Sarang S. Dalal ◽  
...  
Keyword(s):  
Interference Suppression ◽  
Minimum Variance ◽  
Systematic Evaluation ◽  
Localization Error ◽  
Somatosensory Stimulation ◽  
Low Snr ◽  
Accurate Localization ◽  
Signal Space Separation ◽  
Linearly Constrained ◽  
Moving Phantom

AbstractBeamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (FieldTrip, SPM12, Brainstorm, and MNE-Python) using datasets both with and without SSS interference suppression.We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with SNR in all four toolboxes.When applied carefully to MEG data with a typical SNR (3–15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs. We also found that the SNR improvement offered by SSS led to more accurate localization.

scholarly journals A modular structure for implementation of linearly constrained minimum variance beamformers

1991 ◽  
Vol 39 (10) ◽  
pp. 2343-2346 ◽  
Author(s):  
T.-C. Liu ◽  
B. Van Veen
Keyword(s):  
Minimum Variance ◽  
Modular Structure ◽  
Linearly Constrained

scholarly journals Recursive linearly constrained minimum variance estimator in linear models with non-stationary constraints

2018 ◽  
Vol 149 ◽  
pp. 229-235 ◽  
Author(s):  
François Vincent ◽  
Eric Chaumette
Keyword(s):  
Linear Models ◽  
Minimum Variance ◽  
Variance Estimator ◽  
Minimum Variance Estimator ◽  
Linearly Constrained
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