Cargando…
Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals
Emotion recognition is very important for the humans in order to enhance the self-awareness and react correctly to the actions around them. Based on the complication and series of emotions, EEG-enabled emotion recognition is still a difficult issue. Hence, an effective human recognition approach is...
Autores principales: | Bhanumathi, K. S., Jayadevappa, D., Tunga, Satish |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904914/ https://www.ncbi.nlm.nih.gov/pubmed/35282409 http://dx.doi.org/10.1155/2022/3749413 |
Ejemplares similares
-
Improving deep convolutional neural networks with mixed maxout units
por: Zhao, Hui-zhen, et al.
Publicado: (2017) -
Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks
por: Wan, Cen, et al.
Publicado: (2019) -
Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition
por: Li, Qi, et al.
Publicado: (2022) -
An Investigation of Deep Learning Models for EEG-Based Emotion Recognition
por: Zhang, Yaqing, et al.
Publicado: (2020) -
Nuclear Norm Regularized Deep Neural Network for EEG-Based Emotion Recognition
por: Liang, Shuang, et al.
Publicado: (2022)