Dragan F. Dimitrov

TL;DR: 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.

TL;DR: 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.

TL;DR: It is proposed that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research while providing a framework for the development and testing of clinically relevant neuroprostheses.

TL;DR: 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.

TL;DR: It is shown that beneficial neuroplasticity can occur alongside decoder adaptation, yielding performance improvements, skill retention, and resistance to interference from native motor networks.