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Erratum: Balconi, M.; et al. Evidences from Rewarding System, FRN and P300 Effect in Internet-Addiction in Young People SHORT TITLE: Rewarding System and EEG in Internet-Addiction Brain Sciences 2017, 7, 81
Open AccessArticle

Feasibility of Equivalent Dipole Models for Electroencephalogram-Based Brain Computer Interfaces

Department of Computer Science, Eastern Washington University, Cheney, WA 99004, USA
Brain Sci. 2017, 7(9), 118;
Received: 5 August 2017 / Revised: 7 September 2017 / Accepted: 13 September 2017 / Published: 15 September 2017
This article examines the localization errors of equivalent dipolar sources inverted from the surface electroencephalogram in order to determine the feasibility of using their location as classification parameters for non-invasive brain computer interfaces. Inverse localization errors are examined for two head models: a model represented by four concentric spheres and a realistic model based on medical imagery. It is shown that the spherical model results in localization ambiguity such that a number of dipolar sources, with different azimuths and varying orientations, provide a near match to the electroencephalogram of the best equivalent source. No such ambiguity exists for the elevation of inverted sources, indicating that for spherical head models, only the elevation of inverted sources (and not the azimuth) can be expected to provide meaningful classification parameters for brain–computer interfaces. In a realistic head model, all three parameters of the inverted source location are found to be reliable, providing a more robust set of parameters. In both cases, the residual error hypersurfaces demonstrate local minima, indicating that a search for the best-matching sources should be global. Source localization error vs. signal-to-noise ratio is also demonstrated for both head models. View Full-Text
Keywords: brain–computer interface; electroencephalogram; equivalent source model brain–computer interface; electroencephalogram; equivalent source model
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Schimpf, P.H. Feasibility of Equivalent Dipole Models for Electroencephalogram-Based Brain Computer Interfaces. Brain Sci. 2017, 7, 118.

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