A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues

Abstract

Emotion classification based on physiological signals has become a hot topic in the past decade. Many studies have attempted to classify emotions using various techniques, to discover human emotions accurately. This study focused on listing the most recent studies that have classified emotions based on electroencephalogram (EEG) signals. This study also focused on solving the problems and challenges facing researchers in emotion classification and EEG applications used in several fields. The plan of this study is based on a strategy with three aspects within specific rules: The first aspect is the methods; we chose studies that included new methods to extract features. The second aspect is the data sets. We tried to choose a study that classified the same data set. The third aspect is applications; we have listed many applications of the EEG in several areas. We concluded from this study that detecting human emotions using the EEG signals is one of the most reliable and widely used methods of detecting emotions in the past few years. Also, we have noticed that the EEG can detect human emotions, especially in psychiatry, for example, for epileptic patients whose emotions cannot be extracted using traditional methods such as facial expressions and tone of voice.