Cargando…
Motor Imagery EEG Classification Using Capsule Networks†
Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery elect...
Autores principales: | Ha, Kwon-Woo, Jeong, Jin-Woo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651225/ https://www.ncbi.nlm.nih.gov/pubmed/31252557 http://dx.doi.org/10.3390/s19132854 |
Ejemplares similares
-
Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network
por: Chang, Zhanyuan, et al.
Publicado: (2022) -
Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks
por: Xie, Yu, et al.
Publicado: (2023) -
An improved model using convolutional sliding window-attention network for motor imagery EEG classification
por: Huang, Yuxuan, et al.
Publicado: (2023) -
EEG Classification of Motor Imagery Using a Novel Deep Learning Framework
por: Dai, Mengxi, et al.
Publicado: (2019) -
Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance
por: Batistić, Luka, et al.
Publicado: (2023)