Cargando…
Comparing Features for Classification of MEG Responses to Motor Imagery
BACKGROUND: Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI i...
Autores principales: | Halme, Hanna-Leena, Parkkonen, Lauri |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5161474/ https://www.ncbi.nlm.nih.gov/pubmed/27992574 http://dx.doi.org/10.1371/journal.pone.0168766 |
Ejemplares similares
-
Across-subject offline decoding of motor imagery from MEG and EEG
por: Halme, Hanna-Leena, et al.
Publicado: (2018) -
Publisher Correction: Across-subject offline decoding of motor imagery from MEG and EEG
por: Halme, Hanna-Leena, et al.
Publicado: (2018) -
Adaptive neural network classifier for decoding MEG signals
por: Zubarev, Ivan, et al.
Publicado: (2019) -
The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training
por: Halme, Hanna-Leena, et al.
Publicado: (2022) -
Mapping and decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG
por: Youssofzadeh, Vahab, et al.
Publicado: (2023)