Cargando…
Emotion Recognition Based on Dynamic Energy Features Using a Bi-LSTM Network
Among electroencephalogram (EEG) signal emotion recognition methods based on deep learning, most methods have difficulty in using a high-quality model due to the low resolution and the small sample size of EEG images. To solve this problem, this study proposes a deep network model based on dynamic e...
Autores principales: | Zhu, Meili, Wang, Qingqing, Luo, Jianglin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900638/ https://www.ncbi.nlm.nih.gov/pubmed/35264939 http://dx.doi.org/10.3389/fncom.2021.741086 |
Ejemplares similares
-
EEG-based emotion recognition using hybrid CNN and LSTM classification
por: Chakravarthi, Bhuvaneshwari, et al.
Publicado: (2022) -
Human Activity Recognition Based on Residual Network and BiLSTM
por: Li, Yong, et al.
Publicado: (2022) -
Automatic Modulation Recognition Based on a DCN-BiLSTM Network
por: Liu, Kai, et al.
Publicado: (2021) -
Dynamic Gesture Recognition Model Based on Millimeter-Wave Radar With ResNet-18 and LSTM
por: Zhang, Yongqiang, et al.
Publicado: (2022) -
An online human–robot collaborative grinding state recognition approach based on contact dynamics and LSTM
por: Chen, Shouyan, et al.
Publicado: (2022)