Cargando…
Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network—Feasibility Study
Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve these problems, however, they depend on parameters or...
Autores principales: | Kwon, Moonyoung, Han, Sangjun, Kim, Kiwoong, Jun, Sung Chan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928936/ https://www.ncbi.nlm.nih.gov/pubmed/31816868 http://dx.doi.org/10.3390/s19235317 |
Ejemplares similares
-
Instant multicolor super-resolution microscopy with deep convolutional neural network
por: Wang, Songyue, et al.
Publicado: (2021) -
Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images
por: Mukherjee, Lopamudra, et al.
Publicado: (2019) -
Sparse convolutional neural network for high-resolution skull shape completion and shape super-resolution
por: Li, Jianning, et al.
Publicado: (2023) -
Super resolution DOA estimation based on deep neural network
por: Liu, Wanli
Publicado: (2020) -
Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network
por: Du, Xiaofeng, et al.
Publicado: (2018)