EEG Data Classification for Mental State Analysis Using Wavelet Packet Transform and Gaussian Process Classifier

Stress is one of the most common problems that is faced by a majority of the students. Long-term stress can lead to serious health problems, for example, depression, heart disease, anxiety, and sleep disorder. This paper proposes an efficient stress level detection framework to detect the stress in students using Electroencephalogram (EEG) signals. The framework classifies stress into three levels; low stress, medium stress and high stress. In this experiment, EEG data is collected from six subjects by placing two electrodes in the prefrontal region. During each trial, the subject solves arithmetic questions under some time pressure. The EEG data is collected while the subject solves the question. 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. ReliefF feature selection method is used to select the best features for classification. The selected feature set is classified into three categories using Gaussian Classification. The proposed framework effectively classifies the level of stress with an accuracy of 94%.