Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates
It is demonstrated that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters from the electrical activity of frontoparietal neuronal ensembles.
Emergence of a Stable Cortical Map for Neuroprosthetic Control
In this article, the authors show that the neural representation for control of a neuroprosthetic device undergoes a process of consolidation, after which it is stable, readily recalled, and…
Chronic, multisite, multielectrode recordings in macaque monkeys
- M. Nicolelis, D. Dimitrov, S. Wise
- BiologyProceedings of the National Academy of Sciences…
- 5 September 2003
A paradigm is described for recording the activity of single cortical neurons from awake, behaving macaque monkeys using high-density microwire arrays and multichannel instrumentation to benefit neurophysiological investigation of learning, perception, and sensorimotor integration in primates and the development of neuroprosthetic devices.
A Minimally Invasive 64-Channel Wireless μECoG Implant
A microsystem based on electrocorticography (ECoG) that overcomes difficulties, enabling chronic recording and wireless transmission of neural signals from the surface of the cerebral cortex and a simultaneous 3× improvement in power efficiency over the state of the art.
Reversible large-scale modification of cortical networks during neuroprosthetic control
It is found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys and there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity.
Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills
It is suggested that corticostriatal plasticity is necessary for abstract skill learning, and that neuroprosthetic movements capitalize on the neural circuitry involved in natural motor learning.
Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies
- R. Canolty, K. Ganguly, J. Carmena
- BiologyProceedings of the National Academy of Sciences
- 20 September 2010
It is shown that spiking activity in single neurons and neuronal ensembles depends on dynamic patterns of oscillatory phase coupling between multiple brain areas, in addition to the effects of proximal LFP phase, which suggests that neuronal oscillations enable selective and dynamic control of distributed functional cell assemblies.
Microstimulation Activates a Handful of Muscle Synergies
Neural Dust: An Ultrasonic, Low Power Solution for Chronic Brain-Machine Interfaces
An ultra-miniature as well as extremely compliant system that enables massive scaling in the number of neural recordings from the brain while providing a path towards truly chronic BMI is proposed.