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Prediction of Individual Finger Forces Based on Decoded Motoneuron Activities

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A Correction to this article was published on 29 April 2019

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Abstract

Accurate prediction of motor output based on neural signals is critical in human–machine interactions. The objective was to evaluate the performance of predicting individual finger forces through an estimation of the descending neural drive to the spinal motoneuron pool. High-density surface electromyogram (EMG) signals of the extensor digitorum communis muscle were obtained, and were then decomposed into individual motor unit discharge events. The frequency of the composite discharge events at the population level was used to derive the descending neural drive, which was then used to predict the finger forces. The global EMG-based approach was used as a control condition. Compared with the EMG-based approach, the experimental results show that the neural-drive-based approach can better predict the individual finger forces with higher R2 values across different force levels and across different force trajectories (steady and varying forces). These findings indicate that the neural drive estimation based on motoneuron firing activities can be used as a reliable neural-machine interface signal involving individual fingers. However, real-time implementation of this approach is needed for future clinical translation.

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Change history

  • 29 April 2019

    Due to an error in production, Figure 4 in the original paper shows the root mean squared error (RMSE) between the force and the neural drive estimation. The <Emphasis Type="Italic">R</Emphasis><Superscript>2</Superscript> heat map as a function of the number of motor units and the accuracy is illustrated below.

Abbreviations

MU:

Motor unit

MUAP:

Motor unit action potential

EMG:

Electromyogram

sEMG:

Surface electromyogram

HD:

High density

EDC:

Extensor digitorum communis

MVC:

Maximal voluntary contraction

SNR:

Signal to noise ratio

ANOVA:

Analysis of variance

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The authors have no financial relationships that may cause a conflict of interest.

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Authors and Affiliations

  1. Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, 27599, USA

    Chenyun Dai, Yizhou Cao & Xiaogang Hu

  2. Medical College of Soochow University, Suzhou, Jiangsu, China

    Yizhou Cao

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  1. Chenyun Dai
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  2. Yizhou Cao
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Correspondence to Xiaogang Hu.

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Associate Editor Jane Grande-Allen oversaw the review of this article.

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Dai, C., Cao, Y. & Hu, X. Prediction of Individual Finger Forces Based on Decoded Motoneuron Activities. Ann Biomed Eng 47, 1357–1368 (2019). https://doi.org/10.1007/s10439-019-02240-1

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