Comparative Analysis of Existing Machine Learning and Deep Learning Algorithms for Emotion Classification using Valence-Arousal Model

Abstract - The Electroencephalogram (EEG) signal- based emotion classification has become an important field in affective computing, and it has been applied to healthcare, education, gaming and adaptive human- computer interaction. The given paper entails a full comparative study of existing machine learning traditional algorithms in the field of recognition of emotions including Valence-Arousal model. EEG signals of standard datasets undergo extensive pre- processing stages such as filtering, segmentation, and elimination of artifact. Machine learning traditional algorithms are compared which includes SVM, k-NN, Random Forest and some basic deep learning models such as CNN, LSTM are evaluated. Findings indicate that the proposed deep learning models (CNN, LSTM) performs well than existing machine learning algorithms because it boosts the robustness of features and high classification accuracy, which is a sign that it is useful in real time and scalable affective computing systems.