Cargando…
Multi-channel EEG emotion recognition through residual graph attention neural network
In this paper, a novel EEG emotion recognition method based on residual graph attention neural network is proposed. The method constructs a three-dimensional sparse feature matrix according to the relative position of electrode channels, and inputs it into the residual network to extract high-level...
Autores principales: | Chao, Hao, Cao, Yiming, Liu, Yongli |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407101/ https://www.ncbi.nlm.nih.gov/pubmed/37559702 http://dx.doi.org/10.3389/fnins.2023.1135850 |
Ejemplares similares
-
Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network
por: Li, Jingcong, et al.
Publicado: (2021) -
Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks
por: Li, Xiang, et al.
Publicado: (2020) -
STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition
por: Li, Jingcong, et al.
Publicado: (2023) -
CR-GCN: Channel-Relationships-Based Graph Convolutional Network for EEG Emotion Recognition
por: Jia, Jingjing, et al.
Publicado: (2022) -
Emotion Recognition from Physiological Channels Using Graph Neural Network
por: Wierciński, Tomasz, et al.
Publicado: (2022)