Cargando…
Multibranch convolutional neural network with contrastive representation learning for decoding same limb motor imagery tasks
INTRODUCTION: Emerging deep learning approaches to decode motor imagery (MI) tasks have significantly boosted the performance of brain-computer interfaces. Although recent studies have produced satisfactory results in decoding MI tasks of different body parts, the classification of such tasks within...
Autores principales: | Phunruangsakao, Chatrin, Achanccaray, David, Izumi, Shin-Ichi, Hayashibe, Mitsuhiro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792600/ https://www.ncbi.nlm.nih.gov/pubmed/36583011 http://dx.doi.org/10.3389/fnhum.2022.1032724 |
Ejemplares similares
-
Effects of visual-electrotactile stimulation feedback on brain functional connectivity during motor imagery practice
por: Phunruangsakao, Chatrin, et al.
Publicado: (2023) -
A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification
por: Altuwaijri, Ghadir Ali, et al.
Publicado: (2022) -
Directional Decoding From EEG in a Center-Out Motor Imagery Task With Visual and Vibrotactile Guidance
por: Hehenberger, Lea, et al.
Publicado: (2021) -
Neural representations for multi-context visuomotor adaptation and the impact of common representation on multi-task performance: a multivariate decoding approach
por: Song, Youngjo, et al.
Publicado: (2023) -
Enhancement of lower limb motor imagery ability via dual-level multimodal stimulation and sparse spatial pattern decoding method
por: Hou, Yao, et al.
Publicado: (2022)