scholarly journals Smart Sensor Design Analysis in Brain Machine Interface using Labview

2011 ◽  
Vol 17 (2) ◽  
pp. 1-5
Author(s):  
Mamatha M.N. ◽  
S. Ramachandran ◽  
M. Chandrasekaran
Keyword(s):  
Design Analysis ◽  
Brain Machine Interface ◽  
Sensor Design ◽  
Smart Sensor ◽  
Machine Interface

A prototype closed-loop brain–machine interface for the study and treatment of pain

2021 ◽  
Author(s):  
Qiaosheng Zhang ◽  
Sile Hu ◽  
Robert Talay ◽  
Zhengdong Xiao ◽  
David Rosenberg ◽  
...  
Keyword(s):  
Closed Loop ◽  
Brain Machine Interface ◽  
Machine Interface

A Multi-Step Neural Control for Motor Brain-Machine Interface by Reinforcement Learning

2013 ◽  
Vol 461 ◽  
pp. 565-569 ◽  
Author(s):  
Fang Wang ◽  
Kai Xu ◽  
Qiao Sheng Zhang ◽  
Yi Wen Wang ◽  
Xiao Xiang Zheng
Keyword(s):  
Reinforcement Learning ◽  
Neural Activity ◽  
Neural Control ◽  
Brain Machine Interface ◽  
Learning Approach ◽  
Complex Task ◽  
Neural Data ◽  
Neural Spikes ◽  
Performance Improvements ◽  
Machine Interface

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.

EEG-based Brain–Machine Interface induces neurorehabilitative recovery of paretic hand in patients with chronic stroke

2009 ◽  
Vol 65 ◽  
pp. S49
Author(s):  
Kimiko Kawashima ◽  
Keiichiro Shindo ◽  
Junichi Ushiba ◽  
Yutaka Tomita ◽  
Yoshihisa Masakado ◽  
...  
Keyword(s):  
Chronic Stroke ◽  
Brain Machine Interface ◽  
Machine Interface
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