Abstract
Brain signal analysis has revolutionized the research on human-computer
interaction. Analyzing brain activity of the human emotions opens
greater avenues to advance the research on Brain signal analysis. Human
emotions play a significant role in social intercourse, human cognition,
and decision making.[1] In this project, Differential Entropy (DE)
features of EEG are used to perform emotion classification. The DE
features are more suited for emotion recognition than Energy spectrum
(ES) features which are used traditionally [2]. We have applied
machine learning algorithms to discriminate three categories of human
emotion: 1) positive 2) neutral and 3) negative. Feature extraction and
dimensionality reduction are performed on the EEG dataset to obtain
high-level features which helped to increase the accuracy and efficiency
of the classification models. We have performed numerous machine
learning models on the EEG data and compared the results of deep
learning models and shallow models. .