Abstract

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.

Highlights

  • The vast majority of non-invasive brain computer interfaces (BCI) based on the electroencephalogram (EEG) focus on temporal events such as sensorimotor rhythms and evoked potentials [1]

  • signal-to-noise ratio (SNR) is explained thethe factdomain that theis domain is finite andsources inverted are to the cortical surface, which creates an upper limit to the possible localization error

  • As the noise restricted to the cortical surface, which creates an upper limit to the possible localization error

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Summary

Introduction

The vast majority of non-invasive brain computer interfaces (BCI) based on the electroencephalogram (EEG) focus on temporal events such as sensorimotor rhythms and evoked potentials [1]. Few attempts have been made to make use of source localization other than by strategic location of the EEG recording electrodes. This is perhaps due to the fact that temporal information in the EEG is well understood, reproducible, and not overly challenging to extract in terms of the required real-time processing power. Few attempts to make use of equivalent source localization for EEG-based BCI have been published

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