Simplifying Multimodal Emotion Recognition with Single Eye Movement Modality

Multimodal emotion recognition has long been a popular topic in affective computing since it significantly enhances the performance compared with that of a single modality. Among all, the combination of electroencephalography (EEG) and eye movement signals is one of the most attractive practices due to their complementarity and objectivity. However, the high cost and inconvenience of EEG signal acquisition severely hamper the popularization of multimodal emotion recognition in practical scenarios, while eye movement signals are much easier to acquire. To increase the feasibility and the generalization ability of emotion decoding without compromising the performance, we propose a generative adversarial network-based framework. In our model, a single modality of eye movements is used as input and it is capable of mapping the information onto multimodal features. Experimental results on SEED series datasets with different emotion categories demonstrate that our model with multimodal features generated by the single eye movement modality maintains competitive accuracies compared to those with multimodality input and drastically outperforms those single-modal emotion classifiers. This illustrates that the model has the potential to reduce the dependence on multimodalities without sacrificing performance which makes emotion recognition more applicable and practicable.