Affective state characterization based on electroencephalography graph-theoretic features

Affective states are typically characterized using spectral power information obtained from electroencephalography (EEG) data collected over specific brain regions. However, while experiencing a complex emotional audio-video stimuli, brain networks transfer information in a highly interactive manner. To characterize this information, we propose using graph theoretical features. Towards this end, first, we established graph theoretical features as meaningful correlates of affective states through Pearson correlation. Then we compared the classification performance of these features with that of conventional spectral power features where percentage increases in classification performance of 7% and 11% were found in arousal and valence, respectively. Moreover, feature level fusion was explored and resulted in better performance as compared to the feature sets alone thus, highlighting the complementarity of EEG graph based features and spectral powers. Overall it is hoped that this study will enhance affective state evaluation via passive brain computer interfaces, thus leading to a plethora of applications such as user experience perception modelling and affective indexing/tagging of videos, to name a few.