Spatial-Temporal Preserving Multimodal Algorithm for EEG-based Emotion Recognition

Abstract

Emotion recognition has been the focus of some human computer interaction researchers. Designing an efficient algorithm for emotion recognition is crucial for its potential use in HCI. Many algorithms have been developed for emotion recognition and a majority of them is based on electroencephalography (EEG). Despite the success of spatial–temporal recurrent neural network (STRNN) and multimodal deep neural networks, they can still be improved. This paper proposes an algorithm which tries to combine STRNN with multimodal deep neural network, where EEG signals are classified using STRNN and eye movement signals are classified using SVM. The results from the two classifiers are fused by max fusion. This paper shows that by taking spatial and temporal information of EEG signals into consideration and using multimodal fusion, the performance of emotion recognition can be improved.