Brain-Machine Interface or Brain-Body Interface? How to Best Exploit the Remarkable Adaptive Competence of the Brain
Robert Ajemian, PhD, Postdoctoral Research Fellow, Laboratory of Emilio Bizzi, McGovern Institute for Brain Research, Massachusetts Institute of Technology, ajemian@mit.edu
Jonathan Winograd, MD, Assistant Professor of Surgery, Harvard Medical School, Plastic and Reconstructive Surgery, Massachusetts General Hospital, jwinograd@partners.org
Moderator: Steven C. Schachter, MD,
Program Leader, CIMIT Neurotechnology Program and CIMIT Site Miner,
Beth Israel Deaconess Medical Center (BIDMC); Professor of Neurology,
Harvard Medical School, Director of Research, Department of Neurology,
BIDMC; Associate Director, Clinical Research, Harvard Medical School
Osher Institute; Member, Board of the Epilepsy Therapy Development Project;
Founder & Editor-in–Chief,
Epilepsy & Behavior; Editor-in-Chief of Epilepsy.com, sschacht@bidmc.harvard.edu
One can imagine engineering two distinct types of brain interfaces:
- Brain-Machine Interfaces -- Brain signals are used to control an artificial device such as a cursor on a computer screen or a robotic arm.
- Brain-Body Interfaces -- The brain signals are used to innervate an individual’s own muscles through an unnatural pathway, e.g., the brain signals control the activation of input to Functional Electrical Stimulating (FES) electrodes implanted in muscles. In this way, the muscles are still the final common pathway of actuation, but the usual route of innervation through the spinal cord is bypassed.
Basic research efforts have focused on brain-machine interfacing, while brain-body interfacing has been largely ignored. This state of affairs seems somewhat counter-intuitive: would it not be potentially advantageous to interface the motor cortex with its original end-organ, muscle, rather than with a machine, since brain circuits have become calibrated for controlling the natural end-effectors of the body? Drs. Winograd and Ajemian will explore the technical problems and conceptual misunderstandings that have steered the field away from brain-body interfacing and towards brain-machine interfacing. They will show how a fully autonomous brain-body interface can be constructed, indicate how such an interface can lead to performance gains in terms of superior generalization capacity and fully autonomous learning (i.e., no need for the always slippery “neural decoder”) and suggest how this technology could be incorporated into therapeutic devices built for humans.