Cargando…
Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks
Robust cross-subject emotion recognition based on multichannel EEG has always been hard work. In this work, we hypothesize that there exist default brain variables across subjects in emotional processes. Hence, the states of the latent variables that relate to emotional processing must contribute to...
Autores principales: | Li, Xiang, Zhao, Zhigang, Song, Dawei, Zhang, Yazhou, Pan, Jingshan, Wu, Lu, Huo, Jidong, Niu, Chunyang, Wang, Di |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061897/ https://www.ncbi.nlm.nih.gov/pubmed/32194367 http://dx.doi.org/10.3389/fnins.2020.00087 |
Ejemplares similares
-
Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition
por: Li, Qi, et al.
Publicado: (2022) -
Exploring EEG Features in Cross-Subject Emotion Recognition
por: Li, Xiang, et al.
Publicado: (2018) -
Multi-channel EEG emotion recognition through residual graph attention neural network
por: Chao, Hao, et al.
Publicado: (2023) -
EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder
por: Liu, Junxiu, et al.
Publicado: (2020) -
Multidimensional Feature in Emotion Recognition Based on Multi-Channel EEG Signals
por: Li, Qi, et al.
Publicado: (2022)