scholarly journals Image-guided Placement of Magnetic Neuroparticles as a Potential High-Resolution Brain-Machine Interface

2018 ◽  
Author(s):  
Irving N. Weinberg ◽  
Lamar O. Mair ◽  
Sahar Jafari ◽  
Jose Algarin ◽  
Jose Maria Benlloch Baviera ◽  
...  
Keyword(s):  
High Resolution ◽  
Brain Machine Interface ◽  
Image Guided ◽  
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.

Magnetic particle composites as a non-invasive high-resolution brain-machine interface (Conference Presentation)

2019 ◽  
Author(s):  
Irving N. Weinberg ◽  
Lamar O. Mair ◽  
Sahar Jafari ◽  
Chad Ropp ◽  
Olivia Hale ◽  
...  
Keyword(s):  
High Resolution ◽  
Magnetic Particle ◽  
Brain Machine Interface ◽  
Non Invasive ◽  
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.

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