The purpose of the present study was to examine the spatial resolution of electroencephalography (EEG) by means of inverse cortical EEG solution. The main interest was to study how the number of measurement electrodes and the amount of measurement noise affects the spatial resolution. A three-layer spherical head model was used to obtain the source-field relationship of cortical potentials and scalp EEG field. Singular value decomposition was used to evaluate the spatial resolution with various measurement noise estimates. The results suggest that as the measurement noise increases the advantage of dense electrode systems is decreased. With low realistic measurement noise, a more accurate inverse cortical potential distribution can be obtained with an electrode system where the distance between two electrodes is as small as 16 mm, corresponding to as many as 256 measurement electrodes. In clinical measurement environments, it is always beneficial to have at least 64 measurement electrodes.
The purpose of the present theoretical study was to examine the spatial resolution of electroencephalography (EEG) by means of the accuracy of the inverse cortical EEG solution. The study focused on effect of the amount of measurement noise and the number of electrodes on the spatial resolution with different resistivity ratios for the scalp, skull and brain. The results show that if the relative skull resistivity is lower than earlier believed, the spatial resolution of different electrode systems is less sensitive to the measurement noise. Furthermore, there is then also greater advantage to be obtained with high-resolution EEG at realistic noise levels.
The spatial resolution of electroencephalography (EEG) is studied by means of inverse cortical EEG solution. Special attention is paid to the effect of electrode density and the effect of measurement noise on the spatial resolution. A three-layer spherical head model is used as a volume conductor to obtain the source-field relationship of cortical potentials and scalp potential field. Effect of measurement noise is evaluated with truncated singular value decomposition (TSVD). Also simulations about different electrode systems' ability to separate cortical sources are performed. The results show that as the measurement noise increases the advantage of dense electrode systems decreases. Our results suggest that in clinical measurement environment it is always beneficial to use at least 64 measurement electrodes. In low-noise realistic measurement environment the use of even 256 measurement electrodes is beneficial.
The purpose of the present study was to evaluate how the brain sources located at different depths can be most effectively measured with bipolar EEG leads. The specificity of an EEG lead to detect sources was studied with a new parameter called region of interest sensitivity ratio (ROISR) by employing a spherical head model. We studied the specificity as a function of electrode distance and further as a function of scalp:skull:brain resistivity ratio. The simulations indicate that the closer to the surface of the brain the source is located, the shorter is the interelectrode distance in the optimal lead. Also in the case of superficial sources, the small misplacement of the electrodes results in a substantial decrease in specificity. The resistivity ratio has the largest effect on the specificity, when the source is located close to the surface of the brain. However in the case of deep sources, the resistivity ratio has only minimal effect on the specificity.
The objective in bioelectric measurements such as ECG and EEG is to register the signal arising from sources in the region of interest. It is also desired that signal-to-noise ratio (SNR) of a measurement is high. The sensitivity of an ideal measurement should focus on and be greater on the target areas in comparison to other areas of the volume conductor. Previously the half-sensitivity volume (HSV) has been applied to describe how focused the measurement is. In this paper we introduce a concept of the half-sensitivity ratio (HSR) which describes how well the sensitivity is concentrated in HSV compared to other source regions i.e. how specific the measurement is to the sources in HSV. Further we may have different region of interests (ROI) to which the measurements are wanted to be specific. Then the concept is called region of interest sensitivity ratio (ROISR). We present here an application of the HSR in analysing sensitivity distributions of bioelectric measurements. We studied the effects of interelectrode distance and the scalp/skull/brain resistivity ratio on the HSR of a bipolar EEG measurement with a threelayer spherical head model. The results indicate that when the focus of interest is on cortical activity more specified and concentrated sensitivity distributions are achieved with smaller interelectrode distances. Further a preliminary measurement with visual evoked potentials provides evidence of the relationship between HSR and SNR of a measurement.
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