Use ofa prioriinformation in estimating tissue resistivities—application to human datain vivo

Abstract

Accurate resistivity values are necessary to construct reliable numerical models to solve forward/inverse problems in EEG and to localize activity centres in functional brain imaging. These models require accurate geometry and resistivity distribution. The geometry may be extracted from high resolution images. The resistivity distribution may be estimated by using a statistically constrained minimum mean squared error estimator algorithm that has been developed previously by Baysal and Eyüboğlu. In this study, the data are obtained by EEG and MEG sensors during SEF/SEP experiments that involve nine human subjects. The numerical model is realistic, subject-specific and the scalp, the skull and the brain resistivities are estimated. By performing nine different estimations, we found average resistivities of 3.183, 64.559 and 2.833 omega m for scalp, skull and brain, respectively, all under 9% standard deviation. The discrepancies between these results and other works are discussed in detail.

In Vivo Conductivity Estimation Using Somatosensory Evoked Potentials and Cortical Constraint on the Source

“…The relative difference between the two is 9%. These values are in the same range as the results of other real experiments [3,4,5,6], and the rather small difference between left and right hand suggests that the results are indeed reproducible. Figure 2.c shows the positions of the dipoles estimated by the method : blue is for the right hand stimulus and red is for the left hand stimulus.…”

“…A different approach is to consider a natural source inside the brain, which allows for more significant measurements of scalp potential. In this case, the electrical source can be controlled by using well-understood stimuli (like median nerve stimulation), combined with MEG for source localization [4,5,6].…”

Influence of skull conductivity perturbations on EEG dipole source analysis

“…Gonçalves et al (2003) stated that the R should be within 20 to 50. Baysal and Haueisen (2004) suggested 23 for the in vivo brain-to-skull conductivity ratio (R ). Lai et al (2005) used cortical imaging technique to estimate in vivo human R and found that most R values are located within 18 to 34.…”

EEG Analysis on Skull Conductivity Perturbations Using Realistic Head Model

“…Baysal and Haueisen [2] found an average in vivo skull -brain conductivity ratio of 1:1/23. Lai et al [11] found the in vivo skull conductivity values would be in the range of 1/18 to 1/34.…”

“…Accurate measurement of EEG requires accurate geometry and conductivity distribution [1] [2]. Among the head tissue layers, skull shows the lowest conductivity due to its complicated bone structure.…”