Brain-Machine Interfaces: Lessons for Prosthetic Hand Control

Alex K Vaskov; Cynthia A Chestek

doi:10.1016/j.hcl.2021.04.003

Brain-Machine Interfaces: Lessons for Prosthetic Hand Control

Hand Clin. 2021 Aug;37(3):391-399. doi: 10.1016/j.hcl.2021.04.003.

Authors

Alex K Vaskov¹, Cynthia A Chestek²

Affiliations

¹ Robotics Institute, University of Michigan, 2505 Hayward St, Ann Arbor, MI 48109, USA.
² Robotics Institute, University of Michigan, 2505 Hayward St, Ann Arbor, MI 48109, USA; Department of Biomedical Engineering, University of Michigan, 2200 Bonisteel Blvd, Ann Arbor, MI 48109, USA; Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Ave, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, 204 Washtenaw Ave, Ann Arbor, MI 48109, USA. Electronic address: cchestek@umich.edu.

PMID: 34253312
DOI: 10.1016/j.hcl.2021.04.003

Abstract

Brain-machine interfaces (BMI) are being developed to restore upper limb function for persons with spinal cord injury or other motor degenerative conditions. BMI and implantable sensors for myoelectric prostheses directly extract information from the central or peripheral nervous system to provide users with high fidelity control of their prosthetic device. Control algorithms have been highly transferable between the 2 technologies but also face common issues. In this review of the current state of the art in each field, the authors point out similarities and differences between the 2 technologies that may guide the implementation of common solutions to these challenges.

Keywords: Brain-machine interfaces; Calibration methods; Fine motor control; Pattern recognition; Regression algorithms.

Publication types

Research Support, N.I.H., Extramural
Review

MeSH terms

Algorithms
Artificial Limbs*
Brain-Computer Interfaces*
Hand
Humans
Spinal Cord Injuries*
Upper Extremity

Grants and funding

R01 NS105132/NS/NINDS NIH HHS/United States