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Stress assessment based on eeg univariate features and functional connectivity measures. New York: Springer.Īlonso, J., Romero, S., Ballester, M., Antonijoan, R., & Mañanas, M. The proposed framework effectively classifies the level of stress with an accuracy of 94%. The selected feature set is classified into three categories using Gaussian Classification. ReliefF feature selection method is used to select the best features for classification. The collected data is pre-processed using a band-pass filter to remove artefacts and appropriate features are extracted through the wavelet packet transform and PyEEG module. The EEG data is collected while the subject solves the question. During each trial, the subject solves arithmetic questions under some time pressure. In this experiment, EEG data is collected from six subjects by placing two electrodes in the prefrontal region. The framework classifies stress into three levels low stress, medium stress and high stress. This paper proposes an efficient stress level detection framework to detect the stress in students using Electroencephalogram (EEG) signals. Long-term stress can lead to serious health problems, for example, depression, heart disease, anxiety, and sleep disorder. Stress is one of the most common problems that is faced by a majority of the students.