G. Van Hoey

Dipole location errors in electroencephalogram source analysis due to volume conductor model errors

An examination is made of dipole location errors in electroencephalogram (EEG) source analysis, due to not incorporating the ventricular system (VS), omitting a hole in the skull and underestimating skull conductivity. The simulations are performed for a large number of test dipoles in 3D using the finite difference method. The maximum dipole location error encountered, utilising 27 and 53 electrodes is 7.6 mm and 6.1 mm, respectively when omitting the VS, 5.6 mm and 5.2 mm, respectively when neglecting the hole in the skull, and 33.4 mm and 28.0 mm, respectively when underestimating skull conductivity. The largest location errors due to neglecting the VS can be found in the vicinity of the VS. The largest location erros due to omitting a hole can be found in the vicinity of the hole. At these positions the fitted dipoles are found close to the hole. When skull conductivity is underestimated, the dipole is fitted close to the skull-brain border in a radial direction for all test dipoles. It was found that the location errors due to underestimating skull conductivity are typically higher than those found due to neglecting the VS or neglecting a hole in the skull.

Influence of measurement noise and electrode mislocalisation on EEG dipole-source localisation

Measurement noise in the electro-encephalogram (EEG) and inaccurate formation about the locations of the EEG electrodes on the head induce localisation errors in the results of EEG dipole source analysis. These errors are studied by performing dipole source localisation for simulated electrode potentials in a spherical head model, for a range of different dipole locations and for two different numbers (27 and 148) of electrodes. Dipole source localisation is performed by iteratively minimising the residual energy (RE), using the simplex algorithm. The ratio of the dipole localisation error (cm) to the noise level (%) of Gaussian measurement noise amounts to 0.15 cm/% and 0.047 cm/% for the 27 and 148 electrode configurations, respectively, for a radial dipole with 40% eccentricity The localisation error due to noise can be reduced by taking into account multiple time instants of the measured potentials. In the case of random displacements of the EEG electrodes, the ratio of dipole localisation errors to electrode location errors amounts to 0.78 cm−1 cm and 0.27 cm−1 cm for the 27 and 148 electrode configurations, respectively. It is concluded that it is important to reduce the measurement noise, and particularly the electrode mislocalisation, as the influence of the latter is not reduced by taking into account multiple time instants.

Automatic localization of EEG electrode markers within 3D MR data

The electrical activity of the brain can be monitored using ElectroEncephaloGraphy (EEG). From the positions of the EEG electrodes, it is possible to localize focal brain activity. Thereby, the accuracy of the localization strongly depends on the accuracy with which the positions of the electrodes can be determined. In this work, we present an automatic, simple, and accurate scheme that detects EEG electrode markers from 3D MR data of the human head.

Inverse calculations in EEG source analysis applying the finite difference method, reciprocity and lead fields

The advances in computer power and memory make the finite difference method (FDM) attractive to solve the Poison differential equation. To reduce the calculation time of the inverse procedure in EEG source analysis, the concept of lead fields in combination with the reciprocity theorem are utilized. First the accuracy of the finite difference method is evaluated in a three-shell spherical head model. The potentials at the 27 EEG scalp electrodes are obtained using the FDM in a cubic grid with node spacing of 2.5 mm. The inverse problem is solved applying the analytical expression. The mean localization error is 2 mm (ranging from 0.3 to 4.5 mm for 18 dipoles). Next, the potentials at the electrodes are given by the analytical expression. The inverse fit is then done utilizing 26 lead fields calculated numerically in a cubic grid with node spacing of 4 mm. The mean localization error is 4.1 mm (ranging from 1.2 mm to 7 mm for 18 dipoles). Finally a realistic head model is used. The potentials at the electrodes, obtained numerically in a grid with node spacing of 2.5 mm, are brought in the inverse procedure. 26 lead fields calculated numerically in a grid with node spacing of 4 mm, are then applied to perform the inverse calculations. The mean localization error is 4 mm (ranging from 0.9 to 7.2 mm for 12 dipoles). These result suggest that the FDM in combination with the lead field concept can be used for EEG source analysis.

Combined detection and source analysis of epileptic EEG spikes

We present a method for the detection of focal epileptic spikes in the EEG (electroencephalogram). The method is based on the dipole source localisation technique and provides a source location estimate for each detected spike. In the first section of the paper, a theoretical introduction is given about dipole source localisation, followed by a description of the modifications that were made to it for a more efficient calculation. An additional decision variable is introduced to exploit the high amplitude of the spikes that are searched for, without however imposing constraints on the precise shape of the waveforms. In the next section the efficiency of the method is discussed on the basis of an experiment with an EEG measurement from an epilepsy patient. It is shown that the method is capable of detecting and localising most of the epileptic spikes present in the EEG, while producing only few false-positive detections.

The realistic versus the spherical head model in EEG dipole source analysis in the presence of noise

The performance of the three-shell spherical head model versus the performance of the realistic head model is investigated when solving the inverse problem with a single dipole, in the presence of noise. This is evaluated by inspecting the average dipole location error when applying a spherical and a realistic head model, for 1000 noisy scalp potentials, originating from the same test dipole and having the same noise level. The location errors are obtained utilizing a local linearization, which is validated with a Monte Carlo simulation. For 27 electrodes, an EEG epoch of one time sample and spatially white Gaussian noise we found that the importance of the realistic head model over the spherical head model reduces by increasing the noise level.

Automatic detection of EEG electrode markers on 3D MR data

The electrical activity of the brain can be monitored using ElectroEncephaloGraphy (EEG). From the positions of the EEG electrodes, it is possible to localize focal brain activity. Thereby, the accuracy of the localization strongly depends on the accuracy with which the positions of the electrodes can be determined. In this work, we present an automatic, simple, and accurate scheme that detects EEG electrode markers from 3D MR data of the human head.