scholarly journals Missing kinaesthesia challenges precise naturalistic cortical prosthetic control

2014 ◽  
Author(s):  
Ferran Galán ◽  
Mark R Baker ◽  
Kai Alter ◽  
Stuart N Baker

A major assumption of brain-machine interface (BMI) research is that patients with disconnected neural pathways can still volitionally recall precise motor commands that could be decoded for naturalistic prosthetic control. However, the disconnected condition of these patients also blocks kinaesthetic feedback from the periphery, which has been shown to regulate centrally generated output responsible for accurate motor control. Here we tested how well motor commands are generated in the absence of kinaesthetic feedback by decoding hand movements from human scalp electroencephalography (EEG) in three conditions: unimpaired movement, imagined movement, and movement attempted during temporary disconnection of peripheral afferent and efferent nerves by ischemic nerve block. Our results suggest that the recall of cortical motor commands is impoverished in absence of kinaesthetic feedback, challenging the possibility of precise naturalistic cortical prosthetic control.

2018 ◽  
Vol 12 ◽  
Author(s):  
Ryohei Fukuma ◽  
Takufumi Yanagisawa ◽  
Hiroshi Yokoi ◽  
Masayuki Hirata ◽  
Toshiki Yoshimine ◽  
...  

Author(s):  
Qiaosheng Zhang ◽  
Sile Hu ◽  
Robert Talay ◽  
Zhengdong Xiao ◽  
David Rosenberg ◽  
...  

2013 ◽  
Vol 461 ◽  
pp. 565-569 ◽  
Author(s):  
Fang Wang ◽  
Kai Xu ◽  
Qiao Sheng Zhang ◽  
Yi Wen Wang ◽  
Xiao Xiang Zheng

Brain-machine interfaces (BMIs) decode cortical neural spikes of paralyzed patients to control external devices for the purpose of movement restoration. Neuroplasticity induced by conducting a relatively complex task within multistep, is helpful to performance improvements of BMI system. Reinforcement learning (RL) allows the BMI system to interact with the environment to learn the task adaptively without a teacher signal, which is more appropriate to the case for paralyzed patients. In this work, we proposed to apply Q(λ)-learning to multistep goal-directed tasks using users neural activity. Neural data were recorded from M1 of a monkey manipulating a joystick in a center-out task. Compared with a supervised learning approach, significant BMI control was achieved with correct directional decoding in 84.2% and 81% of the trials from naïve states. The results demonstrate that the BMI system was able to complete a task by interacting with the environment, indicating that RL-based methods have the potential to develop more natural BMI systems.


2009 ◽  
Vol 65 ◽  
pp. S49
Author(s):  
Kimiko Kawashima ◽  
Keiichiro Shindo ◽  
Junichi Ushiba ◽  
Yutaka Tomita ◽  
Yoshihisa Masakado ◽  
...  

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