Brain-Computer Interaction (BCI) system intelligence has become more dependent on electroencephalogram (EEG)-based emotion recognition because of the numerous applications of emotion classification, such as recommender systems, cognitive load detection, etc.Emotion classification has drawn the recent buzz in Artificial Intelligence (AI)-powered Accessories research.In this article, we presented a systematic review of automated emotion recognition from EEG signals using AI.
The review process is carried out based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA).After that EEG databases, and EEG preprocessing methods are included in this #6.45 COPPER MAHOGANY study.Also included feature extraction and feature selection methods.
In addition, the included studies were divided into two types: i)deep learning(DL)-based emotion identification systems and ii) machine learning(ML)-based emotion classification models.The examined systems are analyzed based on their features, classification methodologies, classifiers, types of classified emotions, accuracy, and the datasets they employed.There is also an interesting comparison, a look at feature research trends, and ideas for new areas to study.