A Novel Human Stress Level Detection Technique Using EEG

In the 21st century, a significant portion of the world's population is plagued by stress. Stress is harmful to humans and can cause various physical and mental illnesses, such as headaches, anxiety, depression, and heart diseases. The aim of this study is to design a machine learning-based model to measure the mental stress level of a person using electroencephalography (EEG) signals of the frontal lobe. The proposed stress level assessment technique can classify the stress level of a person into four categories: no stress, low stress, moderate stress, and high stress. In our study, first EEG signals of the subjects have been recorded while they solve four mathematical question sets with different complexity levels. Subsequently, different handcrafted feature extraction techniques have been employed for extracting eight features, namely Skewness, Kurtosis, Maximum, Mean, Mean Absolute Value, Minimum, Standard Deviation, and Power Spectral Density from the pre-processed EEG signals. A majority voting-based ensemble classifier model has been designed by combining the predictions of three different classifiers, i.e., Support Vector Machine (SVM), K-Nearest neighbour (KNN), and Naive Bayes, to predict a person's mental stress level. The obtained classification accuracy of the proposed ensemble classifier model is 93.85 %.