Identifying valence and arousal levels via connectivity between EEG channels

Implicit emotion tagging is a central theme in the area of affective computing. To this end, Several physiological signals acquired from subjects can be employed, for example, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) from brain, electrocardiography (ECG) from cardiac activities, and other peripheral physiological signals, such as galvanic skin resistance, electromyogram (EMG), blood volume pressure etc. Brain is regarded as the place where emotional activities evoke. Determining affective states by observing brain activities directly is of therefore great interest. There are several published works that use EEG signals to identify affective states in different aspects with various stimuli, e.s., images, musics and videos. In this paper, we propose to adopt EEG connectivity between electrodes to identify subjects' affective levels in both valence and arousal space during video stimuli presentation. Three catagories of connectivity are adopted in magnitude and phase domains. One open accessed affective database, DEAP, is used as benchmark. We will show that with the proposed connectivity-based representation, the accuracy of affective levels identification tasks are higher than the same tasks in existing works based on same database.