A Review on Nonlinear Methods Using Electroencephalographic Recordings for Emotion Recognition

Electroencephalographic (EEG) recordings are receiving growing attention in the field of emotion recognition, since they monitor the brain's first response to an external stimulus. Traditionally, EEG signals have been studied from a linear viewpoint by means of statistical and frequency features. Nevertheless, given that the brain follows a completely nonlinear and nonstationary behavior, linear metrics present certain important limitations. In this sense, the use of nonlinear methods has recently revealed new information that may help to understand how the brain works under a series of emotional states. Hence, this paper summarizes the most recent works that have applied nonlinear methods in EEG signal analysis for emotion recognition. This paper also identifies some nonlinear indices that have not been employed yet in this research area.

Automated summary

Electroencephalographic (EEG) recordings have traditionally been analyzed using linear metrics, but with the recognition of the brain's nonlinear and nonstationary behavior, linear methods have limitations. Recent studies have shown that nonlinear methods can provide new insights into how the brain functions in different emotional states.