4.4 Article

Movement representation in the primary motor cortex and its contribution to generalizable EMG predictions

Journal

JOURNAL OF NEUROPHYSIOLOGY
Volume 109, Issue 3, Pages 666-678

Publisher

AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.00331.2012

Keywords

muscle activity; wrist movement; single-neuron recording; brain-machine interface; electromyography

Funding

  1. National Institute of Neurological Disorders and Stroke (NINDS) [NS-053603]
  2. Chicago Community Trust through the Searle Program for Neurological Restoration
  3. NINDS [F31-NS-071737]

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Oby ER, Ethier C, Miller LE. Movement representation in the primary motor cortex and its contribution to generalizable EMG predictions. J Neurophysiol 109: 666-678, 2013. First published November 14, 2012; doi:10.1152/jn.00331.2012.-It is well known that discharge of neurons in the primary motor cortex (M1) depends on end-point force and limb posture. However, the details of these relations remain unresolved. With the development of brain-machine interfaces (BMIs), these issues have taken on practical as well as theoretical importance. We examined how the M1 encodes movement by comparing single-neuron and electromyographic (EMG) preferred directions (PDs) and by predicting force and EMGs from multiple neurons recorded during an isometric wrist task. Monkeys moved a cursor from a central target to one of eight peripheral targets by exerting force about the wrist while the forearm was held in one of two postures. We fit tuning curves to both EMG and M1 activity measured during the hold period, from which we computed both PDs and the change in PD between forearm postures (Delta PD). We found a unimodal distribution of these Delta PDs, the majority of which were intermediate between the typical muscle response and an unchanging, extrinsic coordinate system. We also discovered that while most neuron-to-EMG predictions generalized well across forearm postures, end-point force measured in extrinsic coordinates did not. The lack of force generalization was due to musculoskeletal changes with posture. Our results show that the dynamics of most of the recorded M1 signals are similar to those of muscle activity and imply that a BMI designed to drive an actuator with dynamics like those of muscles might be more robust and easier to learn than a BMI that commands forces or movements in external coordinates.

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