Emotion Recognition using EEG signal data from EMO2018 Dataset

Abstract

Emotion Recognition (ER) is developing area within the artificial intelligence field that is focused on comprehending and further interpreting of human emotions through various modalities. Despite that, these approaches are often not ubiquitous as they are affected by external factors. With recent physiology research connecting development of emotions to the central nervous system, usage of brain signals became a highly practical option for emotion recognition. One of the most promising methods of emotion recognition using brain signals for emotion recognition involves using Electroencephalography (EEG). Despite being more complex than classical machine learning or deep learning approaches, EEG-based emotion recognition is potentially more accurate and robust, with applications in mental health monitoring, researches in applied physiology or human-computer interactions. This thesis studies existing approaches of EEG-based emotion recognition methods for private EMO2018 dataset. We adopted methods of Fast Fourier Transform with additional processing for key features extraction and tested different Deep Learning models. Our results show performances of utilized Deep learning models with best accuracy of 88.6% from Hybrid Neural Network approach.