Cargando…
A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision
Convolutional neural networks (CNNs), which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic (EEG) signals, especially for decoding brain states in brain-computer interfaces (BCIs). Nevertheless,...
Autores principales: | Borra, Davide, Fantozzi, Silvia, Magosso, Elisa |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295472/ https://www.ncbi.nlm.nih.gov/pubmed/34305550 http://dx.doi.org/10.3389/fnhum.2021.655840 |
Ejemplares similares
-
Decoding P300 Variability Using Convolutional Neural Networks
por: Solon, Amelia J., et al.
Publicado: (2019) -
A separable convolutional neural network-based fast recognition method for AR-P300
por: He, Chunzhao, et al.
Publicado: (2022) -
Multibranch convolutional neural network with contrastive representation learning for decoding same limb motor imagery tasks
por: Phunruangsakao, Chatrin, et al.
Publicado: (2022) -
Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study
por: Qing, Kunqiang, et al.
Publicado: (2021) -
Competitive fitness analysis using Convolutional Neural Network
por: Palka, Joanna K., et al.
Publicado: (2020)