Dae-Young Lee  Young-Seok Choi

10.5626/JOK.2020.47.3.227

Electroencephalogram Emotion Complexity multivariate multiscale fuzzy entropy

An Electroencephalogram (EEG) signal is an immediate and continuous signal that records brain activity, and it is mainly used for emotional analysis since it can directly reflect the changes of human emotional states. Among the methods of analyzing the EEG signals, entropy analysis is one of the measures for quantifying the complexity of time series. This quantitative analysis of complexity is promising for investigating non-stationary and nonlinear physiological signals. In this paper, we propose a multivariate multiscale fuzzy entropy (MMFE) analysis method that quantifies the complexity of multivariate time series over various time scales to analyze emotional states using EEG signals recorded from multiple electrodes as input. A public database, DEAP, is used as input data in this analysis, and the results show the possibility that emotional states can be distinguished through the binary classification of high/low arousal and high/low valence.