A Saliency based Feature Fusion Model for EEG Emotion Estimation
| Autor: | Delvigne, V. (Victor), Facchini, A. (Antoine), Wannous, H. (Hazem), Dutoit, T. (Thierry), Ris, L. (Laurence), Vandeborre, J-P. (Jean-Philippe) |
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| Přispěvatelé: | Université de Mons (UMons), Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Nord Europe), Institut Mines-Télécom [Paris] (IMT), Centre for Digital Systems (CERI SN - IMT Nord Europe), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Université de Mons [UMons], Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe], Centre for Digital Systems [CERI SN - IMT Nord Europe], Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL] |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Emotions Computer Science - Computer Vision and Pattern Recognition Reproducibility of Results Electroencephalography [INFO]Computer Science [cs] |
| Zdroj: | 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Jul 2022, Glasgow, United Kingdom. pp.3170-3174, ⟨10.1109/EMBC48229.2022.9871720⟩ |
| DOI: | 10.1109/EMBC48229.2022.9871720⟩ |
| Popis: | International audience; Among the different modalities to assess emotion, electroencephalogram (EEG), representing the electrical brain activity, achieved motivating results over the last decade. Emotion estimation from EEG could help in the diagnosis or rehabilitation of certain diseases. In this paper, we propose a dual model considering two different representations of EEG feature maps: 1) a sequential based representation of EEG band power, 2) an image-based representation of the feature vectors. We also propose an innovative method to combine the information based on a saliency analysis of the image- based model to promote joint learning of both model parts. The model has been evaluated on four publicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved results outperform results from state-of-the-art approaches for three of the proposed datasets with a lower standard deviation that reflects higher stability. For sake of reproducibility, the codes and models proposed in this paper are available at https://github.com/VDelv/Emotion-EEG.uk |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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