SSTD

A Novel Spatio-Temporal Demographic Network for EEG-Based Emotion Recognition

Bibliographic Data

ID22106946
AuthorsRui Li (0000-0001-9744-7965, Lanzhou University), Chao Ren (0000-0001-5500-7902, Lanzhou University), Chen Li (0000-0001-6150-8594, Lanzhou University), Nan Zhao (0000-0003-3498-4741, Lanzhou University), Dawei Lu (0009-0004-0720-5137, Beijing Institute of Technology), Xiaowei Zhang (0000-0001-6314-654X, Lanzhou University)
Year2023
Volume10
Issue1
Pages376-387
Publication date2023-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2022.3188891
OpenAlexW4285412025
LanguageEN
Citations received4
References cited46

Emotion recognition is the key to making machines more intelligent. This study proposes a novel sing-link end-to-end spatio-temporal demographic network (SSTD) that fuses spatial, temporal, and demographic information for electroencephalography (EEG)-based emotion recognition. In the SSTD model, an adaptive time window using single-link hierarchical clustering based on Riemannian metrics was realized for data preprocessing to solve the problem of individual differences. Then, the preprocessed EEG data acted as a gate recurrent unit (GRU) network input to calculate high-level time-domain features. At the same time, the EEG covariance matrices were fed into the symmetric positive definite matrix network (SPDNet) to calculate high-level spatial features. Given the correlation between EEG signals and individual demographic information, gender and age factors were integrated into the spatio-temporal model, resulting in more effective high-level features for EEG-based emotion recognition. Finally, extensive comparative experiments were conducted on two public datasets: DEAP and DREAMER. The average accuracy of valence and arousal on the DEAP dataset are 68.28% and 71.48%, respectively. The average accuracy of valence and arousal on the DREAMER dataset are 76.81% and 81.64%, respectively. Experimental results show that the SSTD model has an excellent recognition performance

Algorithm · Arousal · Cluster analysis · Covariance matrix · Electroencephalography · Emotion recognition · Preprocessor · Speech recognition · Computer Science · EEG and Brain-Computer Interfaces · Emotion and Mood Recognition · Heart Rate Variability and Autonomic Control · Psychology · Artificial Intelligence