An attempt to evaluate integrally the relationship between the characteristics of the SCP and the SGP was made using multiple correlation and regression analysis. Consider how all the many characteristics of the VEP are related to local SCP in the occipital and frontal areas. A similar choice of SCP parameters was made in order to determine how much the energy characteristics of not only the visual area, but also other parts of the brain are associated with the parameters of the SGP .
Multiple correlation coefficients in different groups of subjects between local SCPs in the frontal and occipital regions and the set of amplitude-time parameters of the SGP
It can be seen that there is a reliable connection between the set of characteristics of the VIZ and local SCPs, and that, in general, with the exception of a group of middle-aged subjects, the multiple correlation coefficients for the occipital and frontal regions do not significantly differ from each other. It is necessary to understand why the local potential in the frontal area, which is not directly connected with the visual system, can correlate with the characteristics of the SGP. High conjugation parameters PEL with energy processes in various brain regions and therefore possible that the processes in the visual system associated with both energy processes in this system, and with the general functional brain state, reflection of which can be e nergoobmen in remote visual system nerve centers.
The characteristics of the SGP and SCP carry different amounts of information, and this explains the so-called asymmetry of correlations. When studying pair correlations, this problem does not arise, because pair correlation is, by definition, symmetric. Another matter is multiple correlation, when variations of one variable (called dependent, or response) are compared with variations of some set of variables (called independent, or factorial characteristics), and on the composition of this The set depends on the presence and quantitative expression of multiple correlation. For example, it is clear that in the presence of mutual correlations within a set of independent variables, the information value of such a set decreases. The number of independent variables also plays a role, since it is clear that the more variables there are, the more likely it is to find such a set of them that could describe variations of the dependent variable. However, not with any large set of variables there is a high multiple correlation. Due to the mutual correlation of various factors, as well as different signs of dispersion, sometimes a smaller set of independent variables can produce higher values of the multiple correlation coefficient. In statistics, this problem is called collinearity.