Augmenting intracortical brain-machine interface with neurally driven error detectors.

Journal: Journal of neural engineering
Published Date: Dec 1, 2017

Abstract

OBJECTIVE: Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs.

Authors

  • Nir Even-Chen
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, United States of America.
  • Sergey D Stavisky
  • Jonathan C Kao
    Dept of Electrical and Computer Engineering, University of California, Los Angeles, CA, 90024, United States.
  • Stephen I Ryu
  • Krishna V Shenoy
    1] Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, California, USA. [2] Departments of Bioengineering and Neurobiology, Stanford Neurosciences Institute and Bio-X Program, Stanford University, Stanford, California, USA.

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