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Learning a novel myoelectric-controlled interface task.

Radhakrishnan SM, Baker SN, Jackson A - J. Neurophysiol. (2008)

Bottom Line: Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability.Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies.Although vibration impaired task performance, it did not affect the rate at which learning occurred.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience, Henry Wellcome Building, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK.

ABSTRACT
Control of myoelectric prostheses and brain-machine interfaces requires learning abstract neuromotor transformations. To investigate the mechanisms underlying this ability, we trained subjects to move a two-dimensional cursor using a myoelectric-controlled interface. With the upper limb immobilized, an electromyogram from multiple hand and arm muscles moved the cursor in directions that were either intuitive or nonintuitive and with high or low variability. We found that subjects could learn even nonintuitive arrangements to a high level of performance. Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability. Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies. In a second experiment, nonvisual feedback from the hand was degraded with amplitude- and frequency-modulated vibration. Although vibration impaired task performance, it did not affect the rate at which learning occurred. We therefore conclude that the motor system can acquire internal models of novel, abstract neuromotor mappings even in the absence of overt movements or accurate proprioceptive signals, but that the distal motor system may be better suited to provide flexible control signals for neuromotor prostheses than structures related to the arm.

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Related in: MedlinePlus

Development of muscle tuning functions during learning of intuitive and nonintuitive arrangements. A: tuning functions derived from the control signals 500 ms after the appearance of the peripheral target. Trials are averaged separately across the 4 quarters of each set to show sequence of changes during learning. Tuning functions were aligned to the appropriate DoA before averaging across muscles and subjects in experiment A. B: equivalent tuning functions calculated from the mean control signal during the peripheral hold period.
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f5: Development of muscle tuning functions during learning of intuitive and nonintuitive arrangements. A: tuning functions derived from the control signals 500 ms after the appearance of the peripheral target. Trials are averaged separately across the 4 quarters of each set to show sequence of changes during learning. Tuning functions were aligned to the appropriate DoA before averaging across muscles and subjects in experiment A. B: equivalent tuning functions calculated from the mean control signal during the peripheral hold period.

Mentions: Figure 5A shows tuning curves for the instantaneous level of control signals 500 ms after the peripheral target appeared. For this analysis, the 192 trials in each set were divided into four consecutive groups of 48 trials to show how tuning patterns evolved during learning. To combine across muscles and subjects, curves were aligned to the DoA before averaging. For the intuitive set, the average tuning curve was peaked around the DoA from the first group of trials onward. Therefore even during the early stages of learning, subjects were correctly able to predict the required muscle combinations soon after the appearance of the peripheral target. By contrast, during nonintuitive control this pattern emerged only as learning progressed. Over the first group of trials (1–48; blue line), the tuning function at 500 ms after target appearance was flat because subjects could not predict the appropriate combination of muscles to activate. However, by the third quarter of trials (97–144; red line) a peak around the DoA appeared, consistent with the development of feedforward cursor control.


Learning a novel myoelectric-controlled interface task.

Radhakrishnan SM, Baker SN, Jackson A - J. Neurophysiol. (2008)

Development of muscle tuning functions during learning of intuitive and nonintuitive arrangements. A: tuning functions derived from the control signals 500 ms after the appearance of the peripheral target. Trials are averaged separately across the 4 quarters of each set to show sequence of changes during learning. Tuning functions were aligned to the appropriate DoA before averaging across muscles and subjects in experiment A. B: equivalent tuning functions calculated from the mean control signal during the peripheral hold period.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2576223&req=5

f5: Development of muscle tuning functions during learning of intuitive and nonintuitive arrangements. A: tuning functions derived from the control signals 500 ms after the appearance of the peripheral target. Trials are averaged separately across the 4 quarters of each set to show sequence of changes during learning. Tuning functions were aligned to the appropriate DoA before averaging across muscles and subjects in experiment A. B: equivalent tuning functions calculated from the mean control signal during the peripheral hold period.
Mentions: Figure 5A shows tuning curves for the instantaneous level of control signals 500 ms after the peripheral target appeared. For this analysis, the 192 trials in each set were divided into four consecutive groups of 48 trials to show how tuning patterns evolved during learning. To combine across muscles and subjects, curves were aligned to the DoA before averaging. For the intuitive set, the average tuning curve was peaked around the DoA from the first group of trials onward. Therefore even during the early stages of learning, subjects were correctly able to predict the required muscle combinations soon after the appearance of the peripheral target. By contrast, during nonintuitive control this pattern emerged only as learning progressed. Over the first group of trials (1–48; blue line), the tuning function at 500 ms after target appearance was flat because subjects could not predict the appropriate combination of muscles to activate. However, by the third quarter of trials (97–144; red line) a peak around the DoA appeared, consistent with the development of feedforward cursor control.

Bottom Line: Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability.Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies.Although vibration impaired task performance, it did not affect the rate at which learning occurred.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience, Henry Wellcome Building, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK.

ABSTRACT
Control of myoelectric prostheses and brain-machine interfaces requires learning abstract neuromotor transformations. To investigate the mechanisms underlying this ability, we trained subjects to move a two-dimensional cursor using a myoelectric-controlled interface. With the upper limb immobilized, an electromyogram from multiple hand and arm muscles moved the cursor in directions that were either intuitive or nonintuitive and with high or low variability. We found that subjects could learn even nonintuitive arrangements to a high level of performance. Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability. Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies. In a second experiment, nonvisual feedback from the hand was degraded with amplitude- and frequency-modulated vibration. Although vibration impaired task performance, it did not affect the rate at which learning occurred. We therefore conclude that the motor system can acquire internal models of novel, abstract neuromotor mappings even in the absence of overt movements or accurate proprioceptive signals, but that the distal motor system may be better suited to provide flexible control signals for neuromotor prostheses than structures related to the arm.

Show MeSH
Related in: MedlinePlus