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Temporal and quantitative variability in muscle electrical activity decreases as dexterous hand motor skills are learned

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  • Toshiyuki Aoyama
  • Yutaka Kohno

Abstract

Muscle activity changes quantitatively and temporally during the motor learning process. However, the association between variability in muscle electrical activity and the learning and performance of dexterous hand movements is not well understood. Therefore, we undertook this study to investigate the relationships between temporal and quantitative variabilities in muscle activity and the learning of motor skills. Thirty-eight healthy participants performed 30 trials of a task that measured the time taken to rotate two cork balls 20 times using their non-dominant hand. The electromyographic (EMG) activities of the abductor pollicis brevis (APB), first dorsal interosseous, and extensor digitorum (ED) muscles were recorded. Temporal and quantitative variabilities in the EMG activity were evaluated by calculating the coefficient of variation of the duration and area of EMG activation. As motor learning proceeded, the task was completed more quickly and the EMG variability decreased. For all three muscles, significant correlations were observed between individual participants’ ball rotation time and EMG variability. Furthermore, significant positive correlations were observed between improvement in ball rotation time and reduction in EMG variability for the APB and ED muscles. These novel findings provide important insights regarding the relationships between temporal and quantitative variabilities in muscle activity and the learning of fine motor skills.

Suggested Citation

  • Toshiyuki Aoyama & Yutaka Kohno, 2020. "Temporal and quantitative variability in muscle electrical activity decreases as dexterous hand motor skills are learned," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-13, July.
  • Handle: RePEc:plo:pone00:0236254
    DOI: 10.1371/journal.pone.0236254
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    Cited by:

    1. Jishu K. Medhi & Pusheng Ren & Mengsha Hu & Xuhui Chen, 2023. "A Deep Multi-Task Learning Approach for Bioelectrical Signal Analysis," Mathematics, MDPI, vol. 11(22), pages 1-17, November.

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