Emotion Recognition using EEG signal data from EMO2018 Dataset
Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
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.
Description
Keywords
EEG, Emotion recognition, Machine learning, Deep learning, Hybrid Neural Networks, CNN, LSTM