Discrete Attractor Pattern Recognition During Resting State in EEG Signal

Abstract

An outstanding open problem in neuroscience is to understand how the brain reacts towards certain stimuli, capable of producing and sustaining in complex spatiotemporal dynamics. Therefore, human brain signals from the electroencephalography (EEG) apparatus are time-varying signals and provide the temporal resolution which describe the dynamic changes in brain. The different positions of electrodes give different time-varying signals. A dynamic correlation between these signals may exist. We conduct the study to identify the group of attractors which occurred during resting state due to the dynamic changes in human brain. To describe the pattern of dynamic, we refer to chaos theory. First, the simulation signals were executed using the Rössler model where this system could produce complex behavior over a range of parameters, thus being capable of capturing multiple observables at the same time. The level of correlation within the generated attractors was defined. By using an EEG signal, the triplet EEG trajectory was generated from the combination of the Binomial matrix of each electrode and each frequency band by cutting the time-series signal throughout the 2s of data. Then the types of attractors that occurred in the 2s of data for each Rs-EC (Resting state -Eyes Close) were observed. Thus, the correlation coefficient of each combination triplet trajectory of EEG signal was measured. Our observations support the view of the brain as a non-equilibrium system in which multistability may arise due to the attractor. The need to identify and classify the human EEG signal into types of attractors was highlighted.