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Stress has become a common emotion that students experience in day to day life. Several factors contribute to their stress and proven to have a detrimental effect on their performance.As a result of higher student demands, inadequate time control, and financial considerations, tension has become pervasive in the academic climate. It has a negative impact on their quality of living, impacting both their physical and emotional well-being. If left untreated for a long time, it may lead to depression and suicidal ideation. Physiological signs and facial expression techniques are used to identify discomfort in the traditional sense. Hormone testing, for example, has the drawback of being invasive.. This study aims to use EEG to detect stress in students because EEG has a strong correlation with stress. The EEG signal is pre-processed to eliminate artefacts, and the Hilbert-Huang Transform is used to retrieve specific time-frequency characteristics. A hierarchical Support Vector Machine (SVM) classifier is used to control the derived features in order to detect stress levels.....The findings demonstrated the system's ability to sense discomfort in real time using their brain waves.
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