Cargando…
Electroencephalogram–Electromyography Coupling Analysis in Stroke Based on Symbolic Transfer Entropy
The coupling strength between electroencephalogram (EEG) and electromyography (EMG) signals during motion control reflects the interaction between the cerebral motor cortex and muscles. Therefore, neuromuscular coupling characterization is instructive in assessing motor function. In this study, to o...
Autores principales: | Gao, Yunyuan, Ren, Leilei, Li, Rihui, Zhang, Yingchun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758532/ https://www.ncbi.nlm.nih.gov/pubmed/29354091 http://dx.doi.org/10.3389/fneur.2017.00716 |
Ejemplares similares
-
Electroencephalogram approximate entropy influenced by both age and sleep
por: Lee, Gerick M. H., et al.
Publicado: (2013) -
Identification of Electroencephalogram Signals in Alzheimer's Disease by Multifractal and Multiscale Entropy Analysis
por: Ando, Momo, et al.
Publicado: (2021) -
Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification
por: Gao, Yunyuan, et al.
Publicado: (2020) -
Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy
por: Li, Zhaohui, et al.
Publicado: (2020) -
Surface Electromyography and Electroencephalogram-Based Gait Phase Recognition and Correlations Between Cortical and Locomotor Muscle in the Seven Gait Phases
por: Wei, Pengna, et al.
Publicado: (2021)