Extracting kinetic information from human motor cortical signals

Robert D. Flint; Po T. Wang; Zachary A. Wright; Christine E. King; Max O. Krucoff; Stephan U. Schuele; Joshua M. Rosenow; Frank P.K. Hsu; Charles Y. Liu; Jack J. Lin; Mona Sazgar; David E. Millett; Susan J. Shaw; Zoran Nenadic; An H. Do; Marc W. Slutzky

doi:10.1016/j.neuroimage.2014.07.049

Extracting kinetic information from human motor cortical signals

Robert D. Flint^*, Po T. Wang, Zachary A. Wright, Christine E. King, Max O. Krucoff, Stephan U. Schuele, Joshua M. Rosenow, Frank P.K. Hsu, Charles Y. Liu, Jack J. Lin, Mona Sazgar, David E. Millett, Susan J. Shaw, Zoran Nenadic, An H. Do, Marc W. Slutzky

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

62 Scopus citations

Abstract

Brain machine interfaces (BMIs) have the potential to provide intuitive control of neuroprostheses to restore grasp to patients with paralyzed or amputated upper limbs. For these neuroprostheses to function, the ability to accurately control grasp force is critical. Grasp force can be decoded from neuronal spikes in monkeys, and hand kinematics can be decoded using electrocorticogram (ECoG) signals recorded from the surface of the human motor cortex. We hypothesized that kinetic information about grasping could also be extracted from ECoG, and sought to decode continuously-graded grasp force. In this study, we decoded isometric pinch force with high accuracy from ECoG in 10 human subjects. The predicted signals explained from 22% to 88% (60. ±. 6%, mean. ±. SE) of the variance in the actual force generated. We also decoded muscle activity in the finger flexors, with similar accuracy to force decoding. We found that high gamma band and time domain features of the ECoG signal were most informative about kinetics, similar to our previous findings with intracortical LFPs. In addition, we found that peak cortical representations of force applied by the index and little fingers were separated by only about 4. mm. Thus, ECoG can be used to decode not only kinematics, but also kinetics of movement. This is an important step toward restoring intuitively-controlled grasp to impaired patients.

Original language	English (US)
Pages (from-to)	695-703
Number of pages	9
Journal	Neuroimage
Volume	101
DOIs	https://doi.org/10.1016/j.neuroimage.2014.07.049
State	Published - Nov 1 2014

Funding

We thank Eric Lindberg, Carolina Carmona, Jun Yao, and Michael Scheid for their assistance in collecting data, and Jules Dewald for providing the force transducer. We also thank Micheal Macken and Elizabeth Gerard for assisting subject recruitment at Northwestern. Finally, we greatly appreciate the assistance of our technicians and of course our subjects for participating in this study. This study was supported by the Brain Research Foundation ( BRF SG 2009-14 ), the Northwestern Memorial Foundation Dixon Translational Research Grant Program (supported in part by NIH grant UL1 RR025741 from the National Center for Research Resources), the Paralyzed Veterans of America (grant # 2728 ), the Doris Duke Charitable Foundation Clinical Scientist Development Award (grant # 2011039 ), the NSF award 1134575 , and an American Brain Foundation grant to AHD.

Keywords

Brain-machine interface
Decoding
EMG
Electrocorticography
Force
Motor cortex

ASJC Scopus subject areas

Neurology
Cognitive Neuroscience

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10.1016/j.neuroimage.2014.07.049

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Flint, R. D., Wang, P. T., Wright, Z. A., King, C. E., Krucoff, M. O., Schuele, S. U., Rosenow, J. M., Hsu, F. P. K., Liu, C. Y., Lin, J. J., Sazgar, M., Millett, D. E., Shaw, S. J., Nenadic, Z., Do, A. H., & Slutzky, M. W. (2014). Extracting kinetic information from human motor cortical signals. Neuroimage, 101, 695-703. https://doi.org/10.1016/j.neuroimage.2014.07.049

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abstract = "Brain machine interfaces (BMIs) have the potential to provide intuitive control of neuroprostheses to restore grasp to patients with paralyzed or amputated upper limbs. For these neuroprostheses to function, the ability to accurately control grasp force is critical. Grasp force can be decoded from neuronal spikes in monkeys, and hand kinematics can be decoded using electrocorticogram (ECoG) signals recorded from the surface of the human motor cortex. We hypothesized that kinetic information about grasping could also be extracted from ECoG, and sought to decode continuously-graded grasp force. In this study, we decoded isometric pinch force with high accuracy from ECoG in 10 human subjects. The predicted signals explained from 22% to 88% (60. ±. 6%, mean. ±. SE) of the variance in the actual force generated. We also decoded muscle activity in the finger flexors, with similar accuracy to force decoding. We found that high gamma band and time domain features of the ECoG signal were most informative about kinetics, similar to our previous findings with intracortical LFPs. In addition, we found that peak cortical representations of force applied by the index and little fingers were separated by only about 4. mm. Thus, ECoG can be used to decode not only kinematics, but also kinetics of movement. This is an important step toward restoring intuitively-controlled grasp to impaired patients.",

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doi = "10.1016/j.neuroimage.2014.07.049",

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AU - Flint, Robert D.

AU - Wang, Po T.

AU - Wright, Zachary A.

AU - King, Christine E.

AU - Krucoff, Max O.

AU - Schuele, Stephan U.

AU - Rosenow, Joshua M.

AU - Hsu, Frank P.K.

AU - Liu, Charles Y.

AU - Lin, Jack J.

AU - Sazgar, Mona

AU - Millett, David E.

AU - Shaw, Susan J.

AU - Nenadic, Zoran

AU - Do, An H.

AU - Slutzky, Marc W.

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AB - Brain machine interfaces (BMIs) have the potential to provide intuitive control of neuroprostheses to restore grasp to patients with paralyzed or amputated upper limbs. For these neuroprostheses to function, the ability to accurately control grasp force is critical. Grasp force can be decoded from neuronal spikes in monkeys, and hand kinematics can be decoded using electrocorticogram (ECoG) signals recorded from the surface of the human motor cortex. We hypothesized that kinetic information about grasping could also be extracted from ECoG, and sought to decode continuously-graded grasp force. In this study, we decoded isometric pinch force with high accuracy from ECoG in 10 human subjects. The predicted signals explained from 22% to 88% (60. ±. 6%, mean. ±. SE) of the variance in the actual force generated. We also decoded muscle activity in the finger flexors, with similar accuracy to force decoding. We found that high gamma band and time domain features of the ECoG signal were most informative about kinetics, similar to our previous findings with intracortical LFPs. In addition, we found that peak cortical representations of force applied by the index and little fingers were separated by only about 4. mm. Thus, ECoG can be used to decode not only kinematics, but also kinetics of movement. This is an important step toward restoring intuitively-controlled grasp to impaired patients.

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Extracting kinetic information from human motor cortical signals

Abstract

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Keywords

ASJC Scopus subject areas

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