EMOTION RECOGNITION WITH ELECTROENCEPHALOGRAPHY USING ARTIFICIAL INTELLIGENCE

Abstract

Emotion recognition has gained major importance in recent years, with applications in human-computer interfaces, affective computing, and numerous medical applications. To capture and analyze the emotional states, several modalities are used, where one of the most dominant is Electroencephalography (EEG). Facilitated by the advancements in EEG acquisition technologies, as well as in the Artificial intelligence field, Emotion Recognition with EEG data has attracted many researchers. This work aims to implement a subject-independent model that utilizes EEG to perform Emotion Recognition on DEAP and DREAMER datasets. It attempts to find the right combination of processing methods, feature extraction, feature selection and classifier that generalize well on unseen data without having excessive computational costs. In this thesis several Machine Learning models are implemented, along with a one-dimensional CNN model which succeeds in providing a reliable performance for the task of Emotion Recognition with EEG.