Effects of dipole position, orientation and noise on the accuracy of EEG source localization

Whittingstall, Kevin; Stroink, G.; Gates, Larry; Connolly, John F.; Finley, G. Allen

doi:10.1186/1475-925x-2-14

Cited by 74 publications

(44 citation statements)

References 20 publications

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“…Then, in conjunction with the actual EEG data measured at specified positions of (usually less than 100) electrodes on the scalp, it can be used to work back and estimate the sources that fit these measurements – the inverse problem. The accuracy with which a source can be located is affected by a number of factors including head-modelling errors, source-modelling errors and EEG noise (instrumental or biological) [ 3 ]. The standard adopted by Baillet et.…”

Section: Introductionmentioning

confidence: 99%

Review on solving the inverse problem in EEG source analysis

Grech

Cassar

Muscat

et al. 2008

J NeuroEngineering Rehabil

944

778

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In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

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Section: Introductionmentioning

confidence: 99%

Review on solving the inverse problem in EEG source analysis

Grech

Cassar

Muscat

et al. 2008

J NeuroEngineering Rehabil

944

778

View full text Add to dashboard Cite

show abstract

“…Additionally, a number of possible model configurations that fit well with the spatial patterns of scalp EEG potentials are needed44. It is obvious that many of the above factors will affect the accuracy with which a source can be localized42434546. Furthermore, theoretically, only an infinite number of recording electrodes could obtain the unique location of each of the responsible sources47.…”

Section: Discussionmentioning

confidence: 99%

Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

Gao

Yang

Huang

et al. 2016

Sci Rep

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To better characterize the cognitive processes and mechanisms that are associated with deception, wavelet coherence was employed to evaluate functional connectivity between different brain regions. Two groups of subjects were evaluated for this purpose: 32 participants were required to either tell the truth or to lie when facing certain stimuli, and their electroencephalogram signals on 12 electrodes were recorded. The experimental results revealed that deceptive responses elicited greater connectivity strength than truthful responses, particularly in the θ band on specific electrode pairs primarily involving connections between the prefrontal/frontal and central regions and between the prefrontal/frontal and left parietal regions. These results indicate that these brain regions play an important role in executing lying responses. Additionally, three time- and frequency-dependent functional connectivity networks were proposed to thoroughly reflect the functional coupling of brain regions that occurs during lying. Furthermore, the wavelet coherence values for the connections shown in the networks were extracted as features for support vector machine training. High classification accuracy suggested that the proposed network effectively characterized differences in functional connectivity between the two groups of subjects over a specific time-frequency area and hence could be a sensitive measurement for identifying deception.

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“…The results of one study suggest that increasing the number of data points to the average enhances the S/N ratio and, consequently, estimations based on averaged potentials with high S/N ratios incorporated with MNI show convergence to typical regions [4]. Whittingstall et al [11] investigated the difference in dipole location between the individual and grand averaged data sets with a standard realistic head model Reliable results of dipole estimation can be obtained from individual averaged potentials with the subject's own head model if the S/N ratio is high. However, we speculated that to increase the S/N ratio, a large number of trials would be needed in order to average the data obtained on one subject.…”

Section: Discussionmentioning

confidence: 99%

Typical dipole locations can be estimated using averaged somatosensory-evoked potentials and a standard brain model

et al. 2009

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It is reasonable to hypothesize that dipoles estimated from grand averaged event-related potentials based on summed-up data obtained from multiple subjects and standard head models could correspond to typical brain regions associated to a particular event. Six healthy subjects were enrolled in a study to test this hypothesis. We estimated dipoles from somatosensory-evoked potentials (SEP) elicited by electrical stimulation to the left median nerve. We also created individual three-layered (scalp, skull, and brain) head models from each subject's magnetic resonance imaging scan, and dipoles were estimated from the individual averaged SEP with each individual head model. We then estimated dipoles using grand averaged SEP across all subjects on the standard head model created from the Montreal Neurological Institute (MNI) standard coordinate system brain template to compare the estimated dipoles located on our own head model and those on the MNI. The dipoles in the post-central gyrus were estimated from negative potentials at 20 ms from the grand averaged data incorporated with the MNI head model, corresponding to a typical location related to SEP stimulation. The results suggest the validity of estimating the dipole location from the grand averaged potential of all subjects with the MNI model if we focus on typical regions related to the task.

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Effects of dipole position, orientation and noise on the accuracy of EEG source localization

Cited by 74 publications

References 20 publications

Review on solving the inverse problem in EEG source analysis

Review on solving the inverse problem in EEG source analysis

Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

Typical dipole locations can be estimated using averaged somatosensory-evoked potentials and a standard brain model

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