Chronic stress is an increasingly critical issue for public health, but the current methods to detect it are often subjective, slow, or inadequate for a real-time application. This thesis proposes a system for the automatic analysis of mental stress based on electroencephalographic (EEG) signals, leveraging a convolutional neural network from the EEGNet architecture. The model was optimised to achieve high performance in terms of both accuracy and processing speed, to enable real-time integration in wearable and portable devices. The model has been trained on the public SAM40 dataset, and its hyperparameters were fine-tuned using K-fold cross-validation. To evaluate generalisation capabilities, the model was also tested on newly collected EEG data specifically acquired for this work.