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

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


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 languageEnglish (US)
Pages (from-to)695-703
Number of pages9
StatePublished - Nov 1 2014
Externally publishedYes


  • Brain-machine interface
  • Decoding
  • Electrocorticography
  • EMG
  • Force
  • Motor cortex

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology
  • General Medicine


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