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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
