Academic Journal
EEG Emotion Recognition Supervised by Temporal Features of Video Stimuli.
| Τίτλος: | EEG Emotion Recognition Supervised by Temporal Features of Video Stimuli. |
|---|---|
| Συγγραφείς: | Ran S, Zhong W, Duan D, Hu F, Ye L, Zhang Q |
| Πηγή: | Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2024 Jul; Vol. 2024, pp. 1-7. |
| Τύπος έκδοσης: | Journal Article; Validation Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: [IEEE] Country of Publication: United States NLM ID: 101763872 Publication Model: Print Cited Medium: Internet ISSN: 2694-0604 (Electronic) Linking ISSN: 23757477 NLM ISO Abbreviation: Annu Int Conf IEEE Eng Med Biol Soc Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [Piscataway, NJ] : [IEEE], [2007]- |
| Ιατρικοί όροι (MeSH): | Emotions*/physiology , Electroencephalography* , Models, Neurological* , Pattern Recognition, Physiological* , Signal Processing, Computer-Assisted*, Algorithms ; Humans ; Male ; Female ; Young Adult ; Adult |
| Περίληψη: | Different types of video stimuli can activate different reactions in the human brain and these signals can be captured and analyzed for emotion recognition applications. However, accurate recognition across subjects is still challenging due to non-stationary and low signal-to-noise ratio of EEG signals. Lying at the intersection of video content analysis, we make an attempt to supervise EEG features with the external evoked video features. An end-to-end framework is proposed by extracting useful emotional representations in EEG signals with the complementarity of video stimuli. For the feature obtaining, an EEG feature extractor and a video feature extractor are combined, along with a cross-modal transformer to align the distributions of the two type features, and then a self-attention mechanism is designed for fusion. The experiments on the subset of DEAP and self-collected datasets validate that the enhancement of EEG features supervised by stimulus information is a reliable solution for subject-independent emotion recognition. |
| Entry Date(s): | Date Created: 20250305 Date Completed: 20250306 Latest Revision: 20250306 |
| Update Code: | 20260130 |
| DOI: | 10.1109/EMBC53108.2024.10781573 |
| PMID: | 40039117 |
| Βάση Δεδομένων: | MEDLINE |
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