Brain–machine interface control of a manipulator using small-world neural network and shared control strategy

2014 ◽  
Vol 224 ◽  
pp. 26-38 ◽  
Author(s):  
Ting Li ◽  
Jun Hong ◽  
Jinhua Zhang ◽  
Feng Guo
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Small World ◽  
Shared Control ◽  
Brain Machine Interface ◽  
Interface Control ◽  
Machine Interface

201 Brain-Machine Interface Control of a Robotic Arm for Object Grasping is Improved With Computer-Vision Based Shared Control

Neurosurgery ◽  
2015 ◽  
Vol 62 ◽  
pp. 233
Author(s):  
Elizabeth C. Tyler-Kabara ◽  
John Downey ◽  
Jeffrey Weiss ◽  
Katharina Muelling ◽  
Arun Venkataraman ◽  
...  
Keyword(s):  
Computer Vision ◽  
Shared Control ◽  
Robotic Arm ◽  
Brain Machine Interface ◽  
Interface Control ◽  
Machine Interface

scholarly journals Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0131491 ◽  
Author(s):  
Iñaki Iturrate ◽  
Jonathan Grizou ◽  
Jason Omedes ◽  
Pierre-Yves Oudeyer ◽  
Manuel Lopes ◽  
...  
Keyword(s):  
Brain Machine Interface ◽  
Task Constraints ◽  
Interface Control ◽  
Related Potentials ◽  
Machine Interface

scholarly journals Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex

Neuron ◽  
2019 ◽  
Vol 102 (3) ◽  
pp. 694-705.e3 ◽  
Author(s):  
Sofia Sakellaridi ◽  
Vassilios N. Christopoulos ◽  
Tyson Aflalo ◽  
Kelsie W. Pejsa ◽  
Emily R. Rosario ◽  
...  
Keyword(s):  
Brain Machine Interface ◽  
Interface Control ◽  
Machine Interface ◽  
Intrinsic Variable

Brain-Machine Interface Control via Reinforcement Learning

2007 ◽  
Author(s):  
Jack DiGiovanna ◽  
Babak Mahmoudi ◽  
Jeremiah Mitzelfelt ◽  
Justin C. Sanchez ◽  
Jose C. Principe
Keyword(s):  
Reinforcement Learning ◽  
Brain Machine Interface ◽  
Interface Control ◽  
Machine Interface

scholarly journals Rate-Based Decoding of Images from Multi-Region, High Resolution, Electrode Recordings In the Mouse Visual System

2021 ◽  
Author(s):  
Christopher B Fritz
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Neural Activity ◽  
Deep Neural Network ◽  
Superior Performance ◽  
Brain Machine Interface ◽  
Feed Forward ◽  
Deep Networks ◽  
Feed Forward Neural Networks ◽  
Machine Interface

We hypothesize that deep networks are superior to linear decoders at recovering visual stimuli from neural activity. Using high-resolution, multielectrode Neuropixels recordings, we verify this is the case for a simple feed-forward deep neural network having just 7 layers. These results suggest that these feed-forward neural networks and perhaps more complex deep architectures will give superior performance in a visual brain-machine interface.

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