Emotion Recognition Using Machine Learning Models on EEG Signals

Gizem Yildiz, Önder Yakut

2025

Abstract

This study proposes an emotion recognition model based on EEG signals. The performance of the proposed model was compared with that of various machine learning models. After preprocessing the raw EEG data, Principal Component Analysis (PCA) was applied for dimension reduction. Emotion classification was performed using various classifiers such as LSTM, SVM, DNN, GRU, RNN, XGBoost, Logistic Regression, and Random Forest using the obtained features. As a result of the studies, GRU achieved the most successful result with an accuracy rate of 97.89%. LSTM achieved 96.25%, DNN 97.81%, Random Forest 95.78%, Logistic Regression 94.61%, SVM 95.55%, XGBoost 96.72%, and RNN 95.55% accuracy rates. These results show that emotional states can be classified with high accuracy by effectively processing EEG signals using PCA.

Paper Citation

in Harvard Style

Yildiz G. and Yakut Ö. (2025). Emotion Recognition Using Machine Learning Models on EEG Signals. In Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS; ISBN 978-989-758-783-2, SciTePress, pages 48-53. DOI: 10.5220/0014295700004848

in Bibtex Style

@conference{iceeecs25,
author={Gizem Yildiz and Önder Yakut},
title={Emotion Recognition Using Machine Learning Models on EEG Signals},
booktitle={Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS},
year={2025},
pages={48-53},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014295700004848},
isbn={978-989-758-783-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS
TI - Emotion Recognition Using Machine Learning Models on EEG Signals
SN - 978-989-758-783-2
AU - Yildiz G.
AU - Yakut Ö.
PY - 2025
SP - 48
EP - 53
DO - 10.5220/0014295700004848
PB - SciTePress