A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity

The effective decoding of movement from non-invasive electroencephalography (EEG) is essential for informing several therapeutic interventions, from neurorehabilitation robots to neural prosthetics. Deep neural networks are most suitable for decoding real-time data but their use in EEG is hindered b...

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Detalles Bibliográficos
Autores principales: Kumar, Neelesh, Michmizos, Konstantinos P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776813/
https://www.ncbi.nlm.nih.gov/pubmed/35058514
http://dx.doi.org/10.1038/s41598-022-05079-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776813/
https://www.ncbi.nlm.nih.gov/pubmed/35058514
http://dx.doi.org/10.1038/s41598-022-05079-0

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