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Research Article Open Access
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. Hence, stress becomes ubiquitous in academic environment due to higher expectations in academic achievement, poor time management, and financial concerns. It has an adverse effect on the quality of their life affecting both physical and mental health. It is a guarantor for depression and suicidal risks if left unnoticed over a longer period. The traditional stress detection system is based on physiological signals and facial expression techniques. The major drawback is the uncertainty that arises due to numerous external factors like sweating, room temperature, anxiety. Some methods like hormone analysis have a drawback of invasive procedure. There is a need for a method that is non-invasive, precise, accurate and reliable. Electroencephalography (EEG) is a perfect tool as it is a non-invasive procedure. Also, it receives feedback from stress hormones; it can serve as reliable tool to measure stress. This research work aims to detect stress for students based on EEG as EEG displays a good correlation with stress. The EEG signal is pre-processed to remove artefacts and relevant time-frequency features are extracted using Hilbert-Huang Transform (HHT). The extracted features are manipulated to detect stress levels using hierarchical Support Vector Machine (SVM) classifier. The results revealed the efficiency of the system to detect stress in real time using their brain wave.
Stress detection, Electroencephalography, Hilbert-Huang transform, Support vector machine, Machine learning, #