Feature Extraction for Emotion Recognition: A Review

Abstract

With the increasing role of Artificial Intelligence and ubiquitous computing paradigms, a stage has arrived where human being and machine is interacting seamlessly. However, the users may face issues while interacting with these systems. A more simple and reliable interaction with machines will be possible by recognizing user’s emotions. In order to develop a system that can respond effectively to user’s emotions can be modeled by utilizing the electroencephalogram (EEG) as a bio-signal sensor. The emotion recognition plays a vital role in the area of Human Computer Interaction (HCI) and Brain Computer Interaction (BCI) to provide good interaction between brain and machine. The emotion recognition comprises of three major phases feature extraction, feature selection and classifiers. The present chapter provides an overview of feature extraction techniques utilized by researchers in frequency domain analysis, time domain analysis and time-frequency domain analysis. The chapter also discusses the process, issues and challenges for feature extraction in EEG, the application area of the EEG.