Master Informatique

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Classification des émotions à partir de signaux EEGàl’aide de techniques d’apprentissage profond
(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.