Training in Use of Brain–Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements

Training in Use of Brain–Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements

Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representat...

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Bibliographic Details
Main Authors: Ryohei Fukuma, Takufumi Yanagisawa, Hiroshi Yokoi, Masayuki Hirata, Toshiki Yoshimine, Youichi Saitoh, Yukiyasu Kamitani, Haruhiko Kishima
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-07-01
Series:Frontiers in Neuroscience
Subjects:
brain-machine interface
robotic hand
magnetoencephalography
cortical plasticity
neurofeedback
closed-loop training
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2018.00478/full

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https://www.frontiersin.org/article/10.3389/fnins.2018.00478/full

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