Cargando…
Motor imagery electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer
INTRODUCTION: Motor imagery electroencephalography (MI-EEG) has significant application value in the field of rehabilitation, and is a research hotspot in the brain-computer interface (BCI) field. Due to the small training sample size of MI-EEG of a single subject and the large individual difference...
Autores principales: | Wang, Ximiao, Dai, Xisheng, Liu, Yu, Chen, Xiangmeng, Hu, Qinghui, Hu, Rongliang, Li, Mingxin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196205/ https://www.ncbi.nlm.nih.gov/pubmed/37213929 http://dx.doi.org/10.3389/fnhum.2023.1175399 |
Ejemplares similares
-
Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning
por: Feng, Jin, et al.
Publicado: (2022) -
Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification
por: Collazos-Huertas, Diego, et al.
Publicado: (2020) -
Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks
por: Zhang, Kai, et al.
Publicado: (2020) -
Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
por: Huang, Jing-Shan, et al.
Publicado: (2021) -
A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification
por: Altuwaijri, Ghadir Ali, et al.
Publicado: (2022)