Stress detection from EEG using power ratio

Stress correlates itself as a mental conscious and emotion within a person that influences mental ability and decision-making skills, which results in an inappropriate work. Studies have recently developed to detect the stress in a person while performing different tasks. One of the methods is through Electroencephalograph (EEG). These are the bioelectrical signals generated in a human body while performing the tasks and thus describes the activity of the brain. Any action taken by a person changes the properties of these signals. This present work focuses on the classification of baseline (relax) and stress detection using EEG sub-band power ratio as features. Support vector machine (SVM) classifier with different kernel function parameters and K-nearest neighbor (KNN) classifier with a different number of neighbors with holdout and 10-fold cross-validation technique were used to classify power ratio features in order to detect stress. To evaluate the classifier performance various performance metrics were used. It is observed that KNN with a number of neighbors as one, with Euclidean distance gives better performance in both validation techniques and also anterior frontal channel Fpl that is placed at the left side of the brain itself gives a good accuracy of 99.42%. The performance of the proposed method is verified on a publicly available mental arithmetic dataset where stress is induced while performing the mental cognitive workload i.e., mental serial subtraction.