Cortical potential imaging using L-curve and GCV method to choose the regularisation parameter.

BACKGROUND: The electroencephalography (EEG) is an attractive and a simple technique to measure the brain activity. It is attractive due its excellent temporal resolution and simple due to its non-invasiveness and sensor design. However, the spatial resolution of EEG is reduced due to the low conducting skull. In this paper, we compute the potential distribution over the closed surface covering the brain (cortex) from the EEG scalp potential. We compare two methods - L-curve and generalised cross validation (GCV) used to obtain the regularisation parameter and also investigate the feasibility in applying such techniques to N170 component of the visually evoked potential (VEP) data. METHODS: Using the image data set of the visible human man (VHM), a finite difference method (FDM) model of the head was constructed. The EEG dataset (256-channel) used was the N170 component of the VEP. A forward transfer matrix relating the cortical potential to the scalp potential was obtained. Using Tikhonov regularisation, the potential distribution over the cortex was obtained. RESULTS: The cortical potential distribution for three subjects was solved using both L-curve and GCV method. A total of 18 cortical potential distributions were obtained (3 subjects with three stimuli each - fearful face, neutral face, control objects). CONCLUSIONS: The GCV method is a more robust method compared to L-curve to find the optimal regularisation parameter. Cortical potential imaging is a reliable method to obtain the potential distribution over cortex for VEP data.

The significance of relative conductivity on thin layers in EEG sensitivity distributions.

Volume conductor head models contain thin tissue layers, some of which have highly contrasting conductivity values relative to neighboring tissues. We expound the cerebrospinal fluid (CSF) and the six cortical layers of the gray matter. The dual nature of the CSF competes with the well-known shunting behavior of the skull. The incorporation of the six ultra thin cortical layers demonstrate the significance of the electrical attraction and shunting of lead field currents in multilayered tissues owing to the inherent conductive properties of each tissue. We relate the similar effects of the CSF to the diploë, i.e., the soft bone between the two hard bone layers of the skull. A natural subsequence of this article will allow researchers and clinicians to conceptually understand the measurement sensitivity distribution of a bipolar electroencephalography (EEG) lead. We recommend including the highly conductive thin layers such as the diploë of the skull and the CSF into head models as well as further investigation into the cortical layers I-VI of the gray matter. Comprehensively, when a thin tissue layer differs in relative conductivity from its neighboring layers, it should be included in the model owing to its influence upon the EEG lead fields, i.e., the measurement sensitivity distributions.

The influence of age and skull conductivity on surface and subdermal bipolar EEG leads.

Bioelectric source measurements are influenced by the measurement location as well as the conductive properties of the tissues. Volume conductor effects such as the poorly conducting bones or the moderately conducting skin are known to affect the measurement precision and accuracy of the surface electroencephalography (EEG) measurements. This paper investigates the influence of age via skull conductivity upon surface and subdermal bipolar EEG measurement sensitivity conducted on two realistic head models from the Visible Human Project. Subdermal electrodes (a.k.a. subcutaneous electrodes) are implanted on the skull beneath the skin, fat, and muscles. We studied the effect of age upon these two electrode types according to the scalp-to-skull conductivity ratios of 5, 8, 15, and 30 : 1. The effects on the measurement sensitivity were studied by means of the half-sensitivity volume (HSV) and the region of interest sensitivity ratio (ROISR). The results indicate that the subdermal implantation notably enhances the precision and accuracy of EEG measurements by a factor of eight compared to the scalp surface measurements. In summary, the evidence indicates that both surface and subdermal EEG measurements benefit better recordings in terms of precision and accuracy on younger patients.

EEG/MEG source imaging: methods, challenges, and open issues.

We present the four key areas of research-preprocessing, the volume conductor, the forward problem, and the inverse problem-that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization.

Correlation between live and post mortem skull conductivity measurements.

The skull is a tissue with a widely controversial range of conductivity values. This article correlates live skull conductivity measurements with post mortem conductivity measurements with a scaling factor ranging between 2.5 and 4. The scaling factor is validated by a mathematical model that determines the skull conductivity using saline and cerebrospinal fluid (CSF) conductivities and correlated with published physical live and post mortem skull conductivity measurements which show support for this live-to-post mortem scale factor.