A real-time classification algorithm for emotion detection using portable EEG

Abstract

Military personnel, airplane pilots, and bus drivers often operate in stressful conditions when something unexpected can happen and cause dangerous consequences if they do not respond properly. Additionally, stress adversely affects human decision making abilities, therefore prompt, preferably real-time detection of fear is very important. Based on previous studies for non-portable multi-electrode electroencephalography (EEG) systems the ratio of the power of the slow waves to that of the fast waves increases when a person is relaxed and decreases when s/he is scared. In this study we test small portable EEG and develop algorithms for real time detection of the stressful condition - fear. During the experiment we compare EEG signals of subjects in relaxed state with those in stressed state while they are watching a scene from a scary movie. The ratio of the slow/fast wave powers was measured and the observed pattern was similar to one obtained using a multi-electrode system. We integrate stream-processing algorithms in the system to ensure real-time detection of any changes in mental condition and timely generate the alarm event.