Mental Stress Classification from Brain Signals using MLP Classifier

Abstract

INTRODUCTION: The most common and widespread mental condition that unavoidably affects people's mood and conduct is stress. The physiological reaction to powerful emotional, intellectual, and physical obstacles might be viewed as stress. As a result, early stress detection can result in solutions for potential improvements and ultimate event suppression. OBJECTIVES: To classify mental stress from the EEG signals of humans using an MLP classifier. METHODS: We examine the EEG signal analysis techniques currently in use for detecting mental stress using Multi-layer Perceptron (MLP). RESULTS: The suggested technique has a 95% classification accuracy performance. CONCLUSION: In our study, the use of MLP classifiers for stress detection from EEG signals has shown promising results. The high accuracy and precision of the classifiers, as well as the informative nature of certain EEG frequency bands, suggest that this approach could be a valuable tool for stress detection and management.