Effects of dipole position, orientation and noise on the accuracy of EEG source localization
 Kevin Whittingstall^{1}Email author,
 Gerhard Stroink^{1},
 Larry Gates^{2},
 JF Connolly^{3} and
 Allen Finley^{4}
https://doi.org/10.1186/1475925X214
© Whittingstall et al; licensee BioMed Central Ltd. 2003
Received: 16 January 2003
Accepted: 6 June 2003
Published: 6 June 2003
Abstract
Background
The electroencephalogram (EEG) reflects the electrical activity in the brain on the surface of scalp. A major challenge in this field is the localization of sources in the brain responsible for eliciting the EEG signal measured at the scalp. In order to estimate the location of these sources, one must correctly model the sources, i.e., dipoles, as well as the volume conductor in which the resulting currents flow. In this study, we investigate the effects of dipole depth and orientation on source localization with varying sets of simulated random noise in 4 realistic head models.
Methods
Dipole simulations were performed using realistic head models and using the boundary element method (BEM). In all, 92 dipole locations placed in temporal and parietal regions of the head with varying depth and orientation were investigated along with 6 different levels of simulated random noise. Localization errors due to dipole depth, orientation and noise were investigated.
Results
The results indicate that there are no significant differences in localization error due tangential and radial dipoles. With high levels of simulated Gaussian noise, localization errors are depthdependant. For low levels of added noise, errors are similar for both deep and superficial sources.
Conclusion
It was found that if the signaltonoise ratio is above a certain threshold, localization errors in realistic head models are, on average the same for deep and superficial sources. As the noise increases, localization errors increase, particularly for deep sources.
Background
The localization of electrical activity on the basis of EEG recordings has found several applications in neuroscience. The location of this activity can be estimated from the calculation of inverse solutions in which the location, amplitude and orientation of a source are adjusted to obtain a best fit between the measured EEG's and the calculated potentials produced by the source. This calculation of the magnetic fields and electric potentials at the scalp due to a source in a head model is called the forward problem. As source model, the equivalent current dipole has been widely used as a source representing focal neural activity [1, 2]. Current dipoles do, however, not only impress a current at the source location. They also cause currents to flow in the surrounding tissue, or, socalled volume currents. These currents are influenced by the shape and conductivity of the different tissues. Clearly, the accuracy with which sources can be localized will be affected by a number of factors including sourcemodelling errors, head modelling errors and noise (biological (background EEG, blinking, etc.) and electrical).
The concentric multisphere model is commonly used for modelling the head, although, its simplistic geometry can result in localization errors. Alternative numerical forward solution approaches account for the individual shape of the head layers. One of these approaches is the boundary element method (BEM), where the head model consists of realistically shaped closed layers with different conductivity values. Although this method still has some severe limitations, such as homogeneity and isotropy of the tissues involved, it enables us to replace the spherical model of the head with a more realistically shaped model for EEG source localization.
Several authors have explored the effects of the depth of a dipolar source on localization accuracy using a spherical head model as volume conductor [3–6]. Using an experimentally constructed realistically shaped head model for inverse calculations, Menninghaus et al. [7] considered both radial and tangential dipoles placed between 10 and 30 mm below the brain surface. They found localization errors in the order of 2–4 mm. However, these sources represent activity only near the surface of the brain, which does not represent deep electrical sources, such as those in clinical epilepsy studies [8, 9]. Moreover, the effects of noise were not investigated. Yvert et al. [10] demonstrated the importance of dipole depth using a realistic head model with 32 electrodes for forward and inverse calculations, although the effects of noise were not investigated. In [11], the effects of simulated noise on dipole localization using realistic models were investigated. Here, localization errors were averaged over all dipole positions. It is left to be seen how noise affects dipoles of different depths and regions in realistically shaped head models.
The direction in which the source is orientated (i.e., tangential or radial to the cortical surface) may also play a role in how accurately it can be reconstructed. Previous theoretical studies have used spherical heads to investigate localization errors resulting from radial and tangential orientated dipoles [7, 12, 13]. Menninghaus et al. [7] reported higher localization errors for radial sources than tangential ones in a phantom study. However, the sources were limited to 30 mm below the cortical surface and only placed in the temporal region.
In this report, localization errors resulting from dipoles of different orientation and depth are investigated in simulated noisy environments using the same realistic head model for forward and inverse computations.
Methods
Distribution of the parietal and temporal dipoles was based on the distance between the origin and the intersection of the zaxis and xaxis with the brain surface, respectively. The dipole depth relates to the distance between the dipole location and where the positive zaxis intercepts the brain surface for parietal sources, and negative xaxis for temporal sources.
Average SNR values of potentials resulting from all tangential dipoles. The SNR is defined as the root mean square (RMS) value of the simulated signal across all 50 electrode sites by the RMS simulated noise level. SNR values resulting from radial dipoles are slightly (~5%) lower (not shown here).
RMS Noise (μV)  Average SNR 

0.035  40 
0.052  25 
0.087  18 
0.180  8 
0.430  4 
0.870  2 
Dipole localization was carried out using the MUSIC algorithm [20, 21] on the same realistic head model from which the forward computations were made. For each noise level in Table 1, we used 20 sets of Gaussian noise distributions in order to obtain statistically meaningful results. With these noise sets, the localization procedure was repeated 20 times for each dipole location in each of the 6 noise levels. In all, 44,160 inverse solutions were performed. The resultant dipole locations were compared with the original setting of the test dipole locations to estimate the errors of source localization caused by deep and superficial sources at different noise levels. Errors are defined as the square root of the sum of squares of the errors in the 3 axes between the position of the original dipole and calculated dipole. The results were then averaged for each dipole location, orientation and noise level. In our discussion, localization errors will be summarized as the average error of deep (35–65 mm) and superficial (5–35 mm) sources.
Results
Overall, localization errors increased with dipole depth and level of simulated noise (Figure 3). This trend is similar for sources in both parietal and temporal regions. For RMS noise values below 0.180 μV (8–40 SNR range), localization errors are only moderately influenced by the level of noise, regardless of source depth and orientation. However, as the noise level increases, differences in accuracy between superficial and deep sources begin to emerge. For noise levels above 0.43 μV (2–4 SNR range), average localization errors were 2 and 4 mm for superficial and deep sources respectively. No significant differences in accuracy were found for sources in temporal and parietal regions. However, a slight difference in accuracy between tangential and radial sources (Figure 3) was seen. This difference is largest for deep sources.
Discussion
Figure 2 shows the mean RMS potential value at 50 electrode sites due to radial and tangential dipoles as a function of dipole depth. The difference in averaged potentials between radial and tangential sources is approximately 0.4 μV in shallow regions, and 0.6 μV in deeper regions. Since the average of potentials arising from deep radial sources is smaller in magnitude than that of deep tangential sources, the effects of noise on these smaller potentials may make it, on average, more difficult for the inverse algorithm to deal with. This suggests that the slight decrease in localization accuracy for deep radial sources is not directly due to its orientation, but to its susceptibility to noise. However, since this difference in accuracy only appears at very low SNR values, these small differences are of little clinical value.
Conclusions
Given a set of measured scalp potentials, estimates of the location of neural activity are possible if a source and head model is specified. Sources can be located anywhere from near the surface of the cortex to deep within the brain stem. If the current dipole is used to model neural activity, its orientation can range from being parallel to perpendicular to the measuring surface. In this study, we examined the influences of dipole depth and orientation using an individual realistic head model for forward and inverse calculations. The findings are briefly summarized here:

With the addition of simulated Gaussian noise (SNR < 4), average localization errors are depth dependent, i.e., superficial sources are more accurately estimated than sources deeper within the brain volume. For low values of added noise (SNR > 8), localization errors are fairly constant for both deep and superficial sources. No significant difference in accuracy for radial or tangential sources is to be expected.

In very noisy data (SNR < 4), a slight decrease in accuracy for radial sources can be expected, but not as much for tangential sources. However, EEG data of this quality is rarely used for source analysis.
It can therefore be concluded that, on average, noise plays a role in how accurately sources can be localized when using a realistic head model for forward and inverse calculations (for low levels of noise, the effects of dipole depth, and orientation are negligible). As the noise increases, localization accuracy begins to decrease, particularly for deep sources of radial orientation.
Declarations
Acknowledgements
We would like to thank Dr. P. McGrath for his encouragement, as well as the anonymous reviewers for their helpful comments.
Authors’ Affiliations
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