Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that...

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Detalles Bibliográficos
Autores principales: Pohlmeyer, Eric A., Mahmoudi, Babak, Geng, Shijia, Prins, Noeline W., Sanchez, Justin C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907465/
https://www.ncbi.nlm.nih.gov/pubmed/24498055
http://dx.doi.org/10.1371/journal.pone.0087253

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907465/
https://www.ncbi.nlm.nih.gov/pubmed/24498055
http://dx.doi.org/10.1371/journal.pone.0087253

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