The information processing in the human brain is a complex process and accordingly our brain cells are differently active when thinking. Electroencephalography (EEG) can be used to measure, analyze and graphically display this electrical activity in the brain. In the clinical field, the analysis of the EEG is of great importance, for example during an operation or in the sleep laboratory. The analysis of the different states of consciousness is carried out with the help of algorithms, which mostly work in the frequency spectrum. The aim of the activities in the joint cooperation with the biosignal analysis working group of the Technical University of Munich is to develop parameters that can characterize the state of consciousness in a physically and physiologically meaningful way and the associated analyzes are carried out in the time domain. For this we use the entropy of difference and compare it with the permutation entropy already used in the literature.
In this context, we also deal with the Granger causality and its application to the EEG. This measure describes the amount of information flow between two electrodes by using autoregressive models to assess whether previous information in one electrode helps predict current information in another electrode.