Recent advances and challenges in nonlinear characterization of brain dynamics for automatic recognition of emotional states

Abstract

Automatic recognition of emotions is an emerging field, because it plays a key role to improve current affective human-computer interactions. Although for that purpose a variety of linear methods have been applied to the electroencephalographic (EEG) recording, nonlinear analysis has recently revealed novel and more useful insights about the brain behavior under different emotional states. This work briefly reviews the main progresses in this context, also highlighting the main challenges that will have to be mandatory tackled in future.