Cargando…
A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification
Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate with the outside world via assistive technology. Regrettably, EEG decoding is challenging because of the com...
Autores principales: | Altuwaijri, Ghadir Ali, Muhammad, Ghulam, Altaheri, Hamdi, Alsulaiman, Mansour |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9032940/ https://www.ncbi.nlm.nih.gov/pubmed/35454043 http://dx.doi.org/10.3390/diagnostics12040995 |
Ejemplares similares
-
Electroencephalogram-Based Motor Imagery Signals Classification Using a Multi-Branch Convolutional Neural Network Model with Attention Blocks
por: Altuwaijri, Ghadir Ali, et al.
Publicado: (2022) -
A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification
por: Altuwaijri, Ghadir Ali, et al.
Publicado: (2022) -
Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition
por: Al-Hammadi, Muneer, et al.
Publicado: (2022) -
An improved model using convolutional sliding window-attention network for motor imagery EEG classification
por: Huang, Yuxuan, et al.
Publicado: (2023) -
Date fruit dataset for intelligent harvesting
por: Altaheri, Hamdi, et al.
Publicado: (2019)