Electroencephalography (EEG) is a method of recording the electrical activity of the brain. EEG signal analysis allows the application of electroencephalography in various systems. For this purpose, machine learning methods are increasingly used, of which artificial neural networks stand out. In this paper, a set of EEG signals of healthy subjects and those diagnosed with an affective disorder was used, and the signals were classified into two classes: “healthy” and “not healthy”. A convolutional neural network for signal classification and an autoencoder for feature extraction were implemented. Features obtained by the autoencoder were then used for classification using shallow classification models (SVM, KNN and Adaboost) and for clustering using unsupervised learning algorithms (k-means, agglomerative clustering, and DBSCAN). The convolutional neural network and autoencoder were both trained three times: on an unbalanced dataset, on a dataset balanced using weights and on a dataset balanced by undersampling. The results showed that of the tested models, the convolutional neural network was the most successful in the classification of EEG signals (average accuracy and F1 score of 0,997). Balancing by undersampling had better results than balancing using weights. Clustering showed that features are naturally grouped according to the diagnosis of the subject.