Neuropsychiatric Disorders Classification using EEG Signal and Deep Neural Networks - Mota, Mateus Balda; Oliveira, Alessandro Bof de; Bof, Patricia; Barone, Dante Augusto Couto (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2025)
Electroencephalogram (EEG) is widely used to measure brain activity and serves as a promising tool in the diagnosis of neuropsychiatric disorders such as anxiety, schizophrenia, and epilepsy, among others. We proposed a deep neural network model to classify neuropsychiatric conditions using EEG signals. The data was processed with PSD and FC features using SMOTE method to handle with unbalanced classes and dimensionality reduction using PCA. The model achieved an accuracy of 71.43% and a mean AUC of 0.9383, with better performance in the obsessive compulsive and anxiety disorders classification. At the same time, mood disorder showed to be more challenging in the classification.