Complexity Analysis of EEG Data during Rest State and Visual Stimulus

This paper presents an approach to analyze physiological states by observing sample entropy (SampEn) and composite permutation entropy index (CPEI) in the complexity measure of an electroencephalographic (EEG) time series. Three states are observed (eyes closed (EC), eyes open (EO) and game playing) during 2D game environment: the last state is observed during visual stimulus, whereas the rest of them are in resting states. This analysis provides an insight into EEG data for frontal, parietal, occipital, temporal and central regions. The results show a clear discrimination among physiological conditions based on values of SampEn and CPEI in the brain regions. The physiological states discrimination based on EEG recordings may be performed in clinical settings. In addition, based on the discovery, a real-time time monitoring system is being developed for the road safety issue by observing vehicle drivers'activity and motorbikes on the road in Malaysia.