Emotion Recognition from EEG Signals by Leveraging Stimulus Videos

This paper proposes a new emotion recognition method from electroencephalogram (EEG) signals by leveraging video stimulus as privileged information, which is only required during training. A Restricted Boltzmann Machine (RBM) is adopted to model the intrinsic relations between stimulus videos and users' EEG response, and to generate new EEG features. Then, the support vector machine is used to recognize users' emotion states from the generated EEG features. Experiments on two benchmark databases demonstrate that stimulus videos as the privileged information can help EEG signals construct better feature space, and RBM can model the high-order dependencies between stimulus videos and users' EEG response successfully. Our proposed emotion recognition method leveraging video stimulus as privileged information outperforms the recognition method only from EEG signals.