One promising non-invasive approach to stress diagnosis and quantification is the study of electroencephalography (EEG) data. In this work, we offer a feature extraction-based approach to EEG-based stress analysis. To extract discriminative features from electroencephalogram (EEG) recordings, signal processing and machine learning algorithms are used. This enables us to recognize patterns linked to reacting to stress. These features are used to train classification algorithms for stress detection. In order to help people better understand the levels of stress they are feeling and find ways to cope with it, we aim to improve the accuracy and reliability of stress detection using EEG signals.