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Author Notes:

Correspondence should be addressed to Robert D. Flint at r-flint@northwestern.edu

R.D.F. and M.W.S. designed research; R.D.F., M.C.T., J.W.T., J.M.R., and M.W.S. performed research; R.D.F., K.L., C.P., and M.W.S. analyzed data; R.D.F., K.L., C.P., and M.W.S. wrote the paper.

We thank our research subjects for their participation and Mukta Vaidya for helpful comments on this manuscript.

The authors declare no competing financial interests.


Research Funding:

This work was supported by the Craig H. Neilsen Foundation Fellowship (R.D.F.); an Emory College Computational Neuroscience training grant (K.L.); Burroughs Wellcome Fund Collaborative Research Travel Grant (C.P.); the National Science Foundation Grant NCS 1835364 (to C.P.); the Emory Neuromodulation Technology Innovation Center (C.P.); the Doris Duke Charitable Foundation Clinical Scientist Development Award (M.W.S.); The Northwestern Memorial Foundation Dixon Translational Research Grant Program, supported in part by National Institutes of Health (NIH) Grant UL1RR025741 (to M.W.S.); the Department of Health and Human Services NIH Grant R01NS094748 (to M.W.S.).


  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neurosciences & Neurology
  • cortex
  • electrocorticography
  • grasp
  • human
  • kinematic
  • kinetic
  • HAND

The Representation of Finger Movement and Force in Human Motor and Premotor Cortices


Journal Title:



Volume 7, Number 4


, Pages 1-15

Type of Work:

Article | Final Publisher PDF


The ability to grasp and manipulate objects requires controlling both finger movement kinematics and isometric force in rapid succession. Previous work suggests that these behavioral modes are controlled separately, but it is unknown whether the cerebral cortex represents them differently. Here, we asked the question of how movement and force were represented cortically, when executed sequentially with the same finger. We recorded high-density electrocorticography (ECoG) from the motor and premotor cortices of seven human subjects performing a movement-force motor task. We decoded finger movement [0.7 6 0.3 fractional variance accounted for (FVAF)] and force (0.7 6 0.2 FVAF) with high accuracy, yet found different spatial representations. In addition, we used a state-of-the-art deep learning method to uncover smooth, repeatable trajectories through ECoG state space during the movement-force task. We also summarized ECoG across trials and participants by developing a new metric, the neural vector angle (NVA). Thus, state-space techniques can help to investigate broad cortical networks. Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90 6 6%). Thus, finger movement and force appear to have distinct representations in motor/premotor cortices. These results inform our understanding of the neural control of movement, as well as the design of grasp brain-machine interfaces (BMIs).

Copyright information:

© 2020 Flint et al.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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