Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization
By Roberto D. Pascual-Marqui. 2007
685 citations recorded by the source corpus.
This paper deals with the EEG/MEG neuroimaging problem: given measurements of scalp electric potential differences (EEG: electroencephalogram) and extracranial magnetic fields (MEG: magnetoencephalogram), find the 3D distribution of the generating electric neuronal activity. This problem has no unique solution. Only particular solutions with "good" localization properties are of interest, since neuroimaging is concerned with the localization of brain function. In this paper, a general family of linear imaging methods with exact, zero error localization to point-test sources is presented. One particular member of this family is sLORETA (standardized low resolution brain electromagnetic tomography; Pascual-Marqui, Methods Find. Exp. Clin. Pharmacol. 2002, 24D:5-12; this http URL). It is shown here that sLORETA has no localization bias in the presence of measurement and biological noise. Another member of this family, denoted as eLORETA (exact low resolution brain electromagnetic tomography; Pascual-Marqui 2005: this http URL), is a genuine inverse solution (not merely a linear imaging method) with exact, zero error localization in the presence of measurement and structured biological noise. The general family of imaging methods is further extended to include data-dependent (adaptive) quasi-linear imaging methods, also with the exact, zero error localization property.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/38befc225304024b15ac43e867b90418ca138f8a
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In Cognitive Neuroscience
Citations in the corpus
- Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, neutral): “We briefly outline the procedure and refer to [33] for further details.”
- The functional significance of EEG microstates - Associations with modalities of thinking (2016, neutral): “Therefore, we computed the source of the mean difference between visualization (object- and spatial-visualization) and verbalization, and between tasks (object-, spatial-visualization and verbalization) and no-task resting using eLORETA (Pascual-Marqui, 2007) (Fig.”
- Instantaneous and lagged measurements of linear and nonlinear dependence between groups of multivariate time series: frequency decomposition (2007, methodology): “I have had extremely useful discussions with G. Nolte, who pointed out a number of embarrassing inconsistencies I wrote into the first draft of the previous related technical report (Pascual-Marqui 2007a).”
- Individualized prediction of heroin cue-induced craving using task-based EEG functional connectivity (2026, unknown): “In this study, we utilized the source-space PEC of EEG source signals as a feature 51,52 .”