Emotion recognition from users´ EEG signals with the help of stimulus VIDEOS

In this paper, we propose a novel approach to recognize users´ emotions from electroencephalogram (EEG) signals by using stimulus videos as privileged information, which is only available during training. Firstly, five frequency features are extracted from each channel of the EEG signals, and several audio/visual features are extracted from video stimulus. Secondly, features are selected by statistical analyses. Then, a new EEG feature space is constructed using Canonical Correlation Analysis under the help of video content. Finally, a support vector machine is adopted as the classifier on the constructed EEG feature space. Experimental results on two benchmark databases demonstrate that video content, as the context, can improve the emotion recognition performance when employed as privileged information.