Abstract

Detecting stress is a critical aspect of mental health monitoring. In this project, we explore the potential of deep learning, specifically Convolutional Neural Networks (CNNs), to automatically identify stress levels from Electroencephalography (EEG) signal data. EEG signals, originating from electrodes on the scalp, possess complex spatial and temporal patterns. CNNs, known for their spatial feature extraction capabilities, offer a powerful tool for unveiling intricate stress-related patterns. Our approach involves data pre-processing, division into training and testing sets, and feature standardization. The custom CNN architecture, with three output units representing distinct stress levels, achieves high accuracy in classifying emotional states. This research highlights the applicability of deep learning in mental health, offering a data-driven approach to timely stress detection and improved well-being. The project underscores the growing role of deep learning in addressing mental health challenges.