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Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates.

Sankaranarayani Rajangam1,2, Po-He Tseng1,2, Allen Yin2,3

  • 1Department of Neurobiology, Duke University Medical Center, Durham, NC.

Scientific Reports
|March 4, 2016
PubMed
Summary
This summary is machine-generated.

Rhesus monkeys learned to control a robotic wheelchair using brain-machine interfaces (BMIs) that decoded cortical activity for navigation. This demonstrates the potential for BMIs to restore whole-body mobility in paralyzed individuals.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Robotics

Background:

  • Brain-machine interfaces (BMIs) have enabled primates to control artificial limbs via cortical activity.
  • The potential for cortical ensembles to represent whole-body navigation kinematics for BMI control remains unexplored.

Purpose of the Study:

  • To investigate if cortical activity can be used to operate a BMI for continuous wheelchair navigation.
  • To determine if rhesus monkeys can learn to navigate a robotic wheelchair using their cortical signals.

Main Methods:

  • Wireless recordings from premotor and sensorimotor cortical neurons in two rhesus monkeys.
  • Training a linear decoder to extract 2D wheelchair kinematics from neural activity during passive navigation.
  • Utilizing the wireless BMI for monkeys to control wheelchair translational and rotational velocities.

Main Results:

  • Monkeys successfully learned to navigate a robotic wheelchair towards a reward using their cortical activity.
  • Cortical neuron populations exhibited tuning to whole-body displacement, enabling navigation.
  • A cortical representation of distance to the reward location was identified.

Conclusions:

  • Cortical ensembles can represent whole-body navigation kinematics.
  • Intracranial BMIs show promise for restoring mobility in severely paralyzed patients.
  • This study advances BMI applications for complex motor tasks beyond limb control.