Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/4117 2018 2018 Northern Technical University, Iraq Abdulrahman Abdulrahman In recent years, EEG based recognition and characterization of brain states has received much interest due to the advances in deep learning and machine learning methods. The non-invasive and highly inexpensive activity of EEG presents a patient with details concerning the activity and the conditions of the brain. The synthesis of artificial intelligence (AI) models (convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and collaborative knowledge options has been explored in a series of studies that recognize the mental state accurately in a large number of cases. The literature focuses on introducing strong, explainable models as well as on multimodal data to boost classification accurateness and reliability. The results are a 1D CNN and a LSTM network were trained separately and in a hybrid, architecture (CNN-LSTM) to classify the EEG signals. The models were appraised using accurateness, accuracy, recollection, F1-score, and confusion matrix analysis. 2026 2026 327 335 10.54216/FPA.210220 https://www.americaspg.com/articleinfo/3/show/4117