EEG emotion recognition using Deep Neural Network (DNN) in virtual reality environments

Agastya, I Made Artha and Marco, Robert and Handayani, Dini Oktarina Dwi (2024) EEG emotion recognition using Deep Neural Network (DNN) in virtual reality environments. Information Technology Journal, 6 (2). pp. 114-121. ISSN 2655-1632 E-ISSN 2655-1438

PDF - Published Version Restricted to Repository staff only Download (2MB) | Request a copy

Abstract

Purpose: The purpose of this study is to explore the integration of EEG technology with virtual reality (VR) systems to enhance therapeutic interventions, improve cognitive state recognition, and develop personalized immersive experiences. Specifically, it investigates the classification of EEG signals in a VR environment using machine learning models and identifies the most effective methods for individual-level analysis. Methods: The study utilized EEG data collected from 31 participants using the Muse 2016 headset, with electrodes positioned according to the 10-20 international system. EEG signals were analyzed for features such as statistical metrics (mean, median, standard deviation, skewness, and kurtosis) and Hjorth parameters (activity, mobility, complexity). Machine learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), were evaluated for their performance in classifying emotional and cognitive states in a VR environment. Result: The results indicate that the Deep Neural Network (DNN) outperformed SVM and KNN models, achieving the highest average classification accuracy. SVM demonstrated consistent performance, with accuracy values consistently above 0.8 across subjects, while KNN showed greater variability and lower overall performance. DNN's architecture, incorporating two hidden layers with ReLU activation and a softmax output layer, demonstrated superior capability in modeling complex EEG patterns. The findings emphasize the effectiveness of DNN in handling high-dimensional and non-linear data, particularly for multi-class classification tasks. Novelty: This study is novel in its focus on personalized machine learning model performance in a VR-EEG setup. Instead of a one-size-fits-all approach, it emphasizes individualized analysis, identifying the most effective model for each participant

Item Type:	Article (Journal)
Uncontrolled Keywords:	EEG, DNN, VR, Brain Signal, Emotion, Classification
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Dini Handayani
Date Deposited:	20 Jan 2025 16:06
Last Modified:	20 Jan 2025 16:06
URI:	http://irep.iium.edu.my/id/eprint/118226

Actions (login required)

View Item