Psychological stress measurement using low cost single channel EEG headset

This paper present, results of the study on noninvasive stress measurement using EEG signals recorded with a single electrode device. The process involves EEG data acquisition, feature extraction, and stress level classification. Psychologists have developed over a period of time, questionnaires that cover a wide range of symptoms associated with stress. In the first step, stress level of each participant was assessed using the Perceived Stress Scale (PSS) questionnaire. EEG signals of twenty eight participants were recorded using a single channel EEG headset for duration of three minutes. Feature vector based on frequency sub bands is used to train three different machine learning algorithms, to classify the stress level of participants. It is evident from results that psychological stress level can be measured by single channel EEG headset using machine learning algorithms with considerable accuracy. Moreover, increased Beta activity of subjects with high stress has been observed as compared to the subjects with no stress. This fact can be used as a key factor in classifying psychological stress with single channel EEG headset.