Faculté des mathématiques et de l'informatique
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Item Classification des Signaux EEG basée sur l’Apprentissage automatique et l'apprentissage profond(university of bordj bou arreridj, 2025) Temhachet Rayane; Laichaoui YasminaApproximately 50 million people worldwide suffer from epilepsy, a chronic neurological disorder. The automatic detection of epileptic seizures from EEG (electroencephalogram) signals remains a major challenge for researchers. This study proposes a methodology for classifying EEG signals using machine learning techniques, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), the Naive Bayes (NB) algorithm, and a Convolutional Neural Network (CNN). To evaluate model performance, metrics such as Accuracy (AC), Sensitivity (SE), Specificity (SP), and Receiver Operating Characteristic (ROC) curves were used. The results demonstrate the effectiveness of the applied models, particularly the CNN, in the automated detection of epilepsy from EEG signals.Item Classification des émotions à partir de signaux EEGàl’aide de techniques d’apprentissage profond(university of bordj bou arreridj, 2025) BELARBI Chaima; KEDJOUTI AmelIn 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.