The Use of Correlation Analysis in Processing Neuroelectric Data

Abstract

Publisher Summary Different forms of correlation analysis, such as auto- and cross-correlation analysis, mainly developed in the field of statistics, have become important tools in various other types of signal processing. This chapter presents several examples of the use of correlation analysis of electroencephalography (EEG) signals and of the use of a similar technique adapted to the study of unit activity. In the study of ontogenetic development of cortical interhemispheric functions, cross-correlation analysis has been used to demonstrate the dependance of interhemispheric synchrony on fetal age, type of activity, and certain structures in the brain. A cross-correlation analysis provides a quantitative measure of the magnitude of the common components of two signals, such as obtained in simultaneous EEG recordings from two points on the skull. It is calculated in a way similar to autocorrelation, the only difference being that two different signals are used. As in the case of autocorrelation, cross-correlation has its spectral analogy usually called cross-spectrum.