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Continuous emotion detection using EEG signals and facial expressions

Emotions play an important role in how we select and consume multimedia. Recent advances on affect detection are focused on detecting emotions continuously. In this paper, for the first time, we continuously detect valence from electroencephalogram (EEG) signals and facial expressions in response to videos. Multiple annotators provided valence levels continuously by watching the frontal facial videos of participants who watched short emotional videos. Power spectral features from EEG signals as well as facial fiducial points are used as features to detect valence levels for each frame continuously. We study the correlation between features from EEG and facial expressions with continuous valence. We have also verified our model's performance for the emotional highlight detection using emotion recognition from EEG signals. Finally the results of multimodal fusion between facial expression and EEG signals are presented. Having such models we will be able to detect spontaneous and subtle affective responses over time and use them for video highlight detection.