The impact of task context on predicting finger movements in a brain-machine interface
- University of Michigan-Ann Arbor, United States
Abstract
A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patient's own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that decoders trained in one context did not generalize well to other contexts, leading to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either the decoder training task context or the hand's physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts. This shift in neural activity possibly explains biases to off-context kinematic predictions and suggests a feature that could help predict different magnitude muscle activations while producing similar kinematics.
Data availability
Neural, behavioral, EMG, and online BMI performance data has been deposited in the Dryad repository (https://doi.org/10.5061/dryad.p2ngf1vtn).
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Data from: The impact of task context on predicting finger movements in a brain-machine interfaceDryad Digital Repository, doi:10.5061/dryad.p2ngf1vtn.
Article and author information
Author details
Funding
National Science Foundation (Grant Number 1926576)
- Matthew J Mender
- Hisham Temmar
- Parag Patil
- Cynthia A Chestek
National Institute of General Medical Sciences (Grant Number R01GM111293)
- Parag Patil
- Cynthia A Chestek
National Science Foundation (Graduate Research Fellowship Program)
- Joseph T Costello
Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant Number F31HD098804)
- Samuel R Nason-Tomaszewski
National Institute of Neurological Disorders and Stroke (Grant Number T32NS007222)
- Matthew S Willsey
National Institute of Neurological Disorders and Stroke (Grant Number R01NS105132)
- Nishant Ganesh Kumar
- Theodore A Kung
The D. Dan and Betty Kahn Foundation (Grant AWD011321)
- Dylan M Wallace
University of Michigan Robotics Institute (Graduate student fellowship)
- Dylan M Wallace
A. Alfred Taubman Medical Research Institute
- Parag Patil
Craig H. Neilsen Foundation (Project 315108)
- Cynthia A Chestek
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All protocols were in accord with the National Institutes of Health guidelines and approved by the University of Michigan Institutional Animal Care and Use Committee (protocol numbers PRO00010076 and PRO00008138).
Copyright
© 2023, Mender et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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The impact of task context on predicting finger movements in a brain-machine interface
eLife 12:e82598.
https://doi.org/10.7554/eLife.82598
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