(university of bordj bou arreridj, 2025) BELARBI Chaima; KEDJOUTI Amel
In recent years, human emotion recognition from EEG signals has seen substantial progress,
largely driven by advances in deep learning techniques. Unlike traditional methods based on
facial expressions, EEG signals provide greater objectivity and robustness against voluntary
manipulations. In this study, we propose an automatic emotion classification approach based
on EEGsignals, utilizing the EEG Brainwave Dataset and the eeg-dataset-emotions. Two deep
learning architectures—1D Convolutional Neural Network (1D CNN) and Long Short-Term
Memory (LSTM) networks—were implemented to extract spatial and temporal features, res
pectively. The experimental results demonstrate that the combined use of these models en
hances the accuracy and reliability of emotional state recognition.
