EEG source localization: Sensor density and head surface coverage

Song, Jasmine; Davey, Colin; Poulsen, Catherine; Luu, Phan; Turovets, Sergei; Anderson, Erik; Li, Kai; Tucker, Don M.

doi:10.1016/j.jneumeth.2015.08.015

Cited by 288 publications

(241 citation statements)

References 61 publications

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“…The tissue conductivity data of three of the four subjects of this study are important because they are widely used as atlases in EEG source localization, for instance the CA and AA based ones in Geosource 3:0 [85], [86], and the AM based BEM in LORETA [87], EEGLAB NFT [88], Brainstorm [89], and FieldTrip [90]; and as an FEM in COMETS [91] and BrainStimulator software released with the SCIRun package [92]. Box plots of the estimates using synthetic data for the scalp (a) and skull (b) conductivity values using the 5 different models and for the four subjects.…”

Section: A New Estimates Including Atlasesmentioning

confidence: 99%

Skull Modeling Effects in Conductivity Estimates Using Parametric Electrical Impedance Tomography

Fernandez-Corazza

Turovets

Luu

et al. 2018

IEEE Trans. Biomed. Eng.

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Abstract-Objective: To estimate scalp, skull, compact bone and marrow bone electrical conductivity values based on Electrical Impedance Tomography (EIT) measurements, and to determine the influence of skull modeling details on the estimates. Methods: We collected EIT data with 62 current injection pairs and built five 6-8 million finite element (FE) head models with different grades of skull simplifications for four subjects, including three whose head models serve as Atlas in the scientific literature and in commercial equipment (Colin27 and EGI's Geosource atlases). We estimated electrical conductivity of the scalp, skull, marrow bone and compact bone tissues for each current injection pair, each model, and each subject. We also provide subject specific conductivity estimates for widely used Atlas head models.

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Section: A New Estimates Including Atlasesmentioning

confidence: 99%

Skull Modeling Effects in Conductivity Estimates Using Parametric Electrical Impedance Tomography

Fernandez-Corazza

Turovets

Luu

et al. 2018

IEEE Trans. Biomed. Eng.

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“…However, due to the lack of individual MRI scans and electrode localization data, the current source analytical protocol does not allow a sufficiently precise localization of EEG sources for reliably distinguishing any pair of the 16 regions per hemisphere. A likelihood analysis (based on the studies of Baillet et al, 2001;Huang et al, 2016;Pizzagalli, 2007;Plummer et al, 2008;Song et al, 2015) conducted with the assumption of a 1.5 cm leakage distance and 50% overlap criterion showed only one case of significant overlap: the transverse temporal gyrus can overlap with the superior temporal and supramarginal regions. Despite this issue, we decided to employ the above described parcellation, because by using a coarser resolution and thus collapsing large chunks of data would have eliminated a large part of inter-individual differences existing in the data, the primary goal of the study.…”

Section: Eeg Recording and Preprocessingmentioning

confidence: 99%

Functional brain networks underlying idiosyncratic switching patterns in multi-stable auditory perception

2018

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In perceptual multi-stability, perception stochastically switches between alternative interpretations of the stimulus allowing examination of perceptual experience independent of stimulus parameters. Previous studies found that listeners show temporally stable idiosyncratic switching patterns when listening to a multi-stable auditory stimulus, such as in the auditory streaming paradigm. This inter-individual variability can be described along two dimensions, Exploration and Segregation. In the current study, we explored the functional brain networks associated with these dimensions and their constituents using electroencephalography. Results showed that Segregation and its constituents are related to brain networks operating in the theta EEG band, whereas Exploration and its constituents are related to networks in the lower and upper alpha and beta bands. Thus, the dimensions on which individuals' perception differ from each other in the auditory streaming paradigm probably reflect separate perceptual processes in the human brain. Further, the results suggest that networks mainly located in left auditory areas underlie the perception of integration, whereas perceiving the alternative patterns is accompanied by stronger interhemispheric connections.

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“…Source localization accuracy depends on several factors like head-modeling errors [27, 28], source-modeling errors and measurement noise contributions [29]. Also it has been pointed out that the scalp potential needs to be sampled with electrodes evenly and densely distributed along the scalp surface [30]. Localization accuracy increases in a non-linear fashion with the number of electrodes, and estimates indicate that probably no less than 500 electrodes would be needed for an accurate sampling of the surface potential distribution [31, 32].…”

Section: Introductionmentioning

confidence: 99%

Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task

et al. 2016

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Lately, Ensemble Empirical Mode Decomposition (EEMD) techniques receive growing interest in biomedical data analysis. Event-Related Modes (ERMs) represent features extracted by an EEMD from electroencephalographic (EEG) recordings. We present a new approach for source localization of EEG data based on combining ERMs with inverse models. As the first step, 64 channel EEG recordings are pooled according to six brain areas and decomposed, by applying an EEMD, into their underlying ERMs. Then, based upon the problem at hand, the most closely related ERM, in terms of frequency and amplitude, is combined with inverse modeling techniques for source localization. More specifically, the standardized low resolution brain electromagnetic tomography (sLORETA) procedure is employed in this work. Accuracy and robustness of the results indicate that this approach deems highly promising in source localization techniques for EEG data.

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EEG source localization: Sensor density and head surface coverage

Abstract: The most accurate source localization is obtained when the voltage surface is densely sampled over both the superior and inferior surfaces.

Cited by 288 publications

References 61 publications

Skull Modeling Effects in Conductivity Estimates Using Parametric Electrical Impedance Tomography

Skull Modeling Effects in Conductivity Estimates Using Parametric Electrical Impedance Tomography

Functional brain networks underlying idiosyncratic switching patterns in multi-stable auditory perception

Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task

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