Dynamic Stream Selection Network for Subject-Independent EEG-Based Emotion Recognition

• Published in: IEEE sensors journal Vol. 24; no. 12; pp. 19336 - 19343
• Main Authors: Li, Wei, Dong, Jianzhang, Liu, Shuxia, Fan, Lingmin, Wang, Siyi
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Convolutional neural networks; Dynamic selection network; dynamic stream selection network (DSSN); electroencephalogram (EEG); Electroencephalography; Emotion recognition; Emotions; Feature extraction; Sensors; Support vector machines; tri-stream structure; Vectors
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3390799

Due to severe cross-subject data variations in electroencephalogram (EEG) signals, the issue of subject-independent EEG-based emotion recognition remains challenging till today. To cope with this challenge, we propose a novel and effective dynamic stream selection network (DSSN), which can adaptively adjust its structure according to the characteristics of signal data from different individuals for this issue. DSSN consists of a tri-stream structure and a dynamic selection network. The tri-stream structure takes charge of extracting the spatial, the temporal, and the spatio-temporal features, respectively, for emotion classification. The dynamic selection network is responsible for selecting the most suitable stream for every subject. Subject-independent experiments on the benchmarks DEAP, DREAMER, and SEED-IV have readily demonstrated the advantage of DSSN over the related advanced approaches.