Emotional states analyze from scaling properties of EEG signals using hurst exponent for stroke and normal groups

Abstract

Emotion is regulated by the interconnection of the brain network. Each emotion is a different mental state, where the neuronal oscillations differ for different emotions. The EEG signal has been a useful method to analyze emotions. Furthermore, the neuronal oscillation can be observed by analyzing the scaling properties of EEG signal. In this study, the EEG signal was used as the source of emotions of stroke patients and normal subject. The Hurst Exponent (HURST) was estimated from the EEG signal to analyze the auto-correlation of the signal. The estimated HURST indicated that all emotions in this work were exhibit positive correlation in the time scale, also the neuronal oscillation for every emotions experimented were statistically different.