Simulation And Modeling Of Evoked Response Electroencephalograph Signal

Electroencephalograph (EEG) data for normal individuals with eyes-closed and under stimuli is analyzed. The stimuli consisted of photo, audio, motor and mental activity. We use several measures from nonlinear dynamics to analyze and characterize the data. We find that the dynamics of the EEG signal is deterministic and chaotic but it is not a low dimensional chaotic system. The evoked responses lead to a redistribution of strengths relative to eyes-closed data. Basically, strength inαwaves decreases whereas that inβwave increases. We also carried out simulations separately and in combination forδ,θ,αandβwaves to understand the data. From the simulation results, it appears that the characteristics of EEG data are consequences of filtering the data with a relatively small range of frequency (0.5–32 Hz). In view of this, we believe that calculation of known nonlinear measures is not likely to be very useful for studying the dynamics of EEG data. We have also successfully modeled the EEG time series using the concept of state space reconstruction in the framework of artificial neural network. It gives us confidence that one would be able to understand, in a more basic way, how collectivity in EEG signal arises.