ABSTRACT
Emotion plays a very significant role in human's everyday communication. Emotion recognition is an essential activity for a computer to understand the human inner state in the Internet of things (IoT)-based brain–computer interface (BCI) framework. Researchers nowadays are more intent toward emotion detection from electroencephalogram (EEG) signals. In recent years, many researchers have conducted various methods of emotion detection from physiological (EEG) signals. A variety of features are extracted from the EEG signals, and various classification methods are applied to these features. In this chapter, different classifiers based on machine learning such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gaussian Naive Bayes (GNB) with Principal Component Analysis (PCA) are used to simulate the relationship between emotional states and EEG signals of the brain. DEAP dataset is used to test the efficacy of these machine learning techniques for the analysis of emotions using physiological EEG signals and it is realized that SVM with PCA significantly improves accuracy in cases of valence, arousal, and dominance. In the case of liking, KNN with PCA gives better results in terms of accuracy. It is also found that SVM with PCA provides better classification accuracy i.e. 64.06% for valence, 64.30% for arousal, 69.50% for dominance, and 64.06% for liking, respectively.
