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Spatial Filtering Improves EMG Classification Accuracy Following Targeted Muscle Reinnervation

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Abstract

The combination of targeted muscle reinnervation (TMR) and pattern classification of electromyography (EMG) has shown great promise for multifunctional myoelectric prosthesis control. In this study, we hypothesized that surface EMG recordings with high spatial resolution over reinnervated muscles could capture focal muscle activity and improve the classification accuracy of identifying intended movements. To test this hypothesis, TMR subjects with transhumeral or shoulder disarticulation amputations were recruited. Spatial filters such as single differential filters, double differential filters, and various two-dimensional, high-order spatial filters were used, and the classification accuracies for fifteen different movements were calculated. Compared with monopolar recordings, spatially localized EMG signals produced increased accuracy in identifying the TMR patients’ movement intents, especially for hand movements. When the number of EMG signals was constrained to 12, the double differential filters gave 5–15% higher classification accuracies than the filters with lower spatial resolution, but resulted in comparable accuracies to the filters with higher spatial resolution. These results suggest that double differential EMG recordings may further improve the TMR-based neural interface for robust, multifunctional control of artificial arms.

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Acknowledgments

The EMG signal classification code used in this study was provided by Professor Kevin B. Englehart, PhD, PE, of the Institute of Biomedical Engineering, University of New Brunswick, Canada. We thank Aimee Schultz, M.S. for editing the manuscript. This work was supported by the National Institute on Disability and Rehabilitation Research (Grant # H133F060029 & H133F080006), the NIH National Institute of Child and Human Development (Grants # R01 HD043137-01, #R01 HD044798, and # NO1-HD-5-3402), and the Defense Advanced Research Projects.

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Author notes

  1. Guanglin Li

    Present address: Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P.R. China

Authors and Affiliations

  1. Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, 345 E. Superior Street, Suite 1406, Chicago, IL, 60611, USA

    He Huang, Ping Zhou, Guanglin Li & Todd Kuiken

  2. Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA

    He Huang

  3. Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA

    Ping Zhou, Guanglin Li & Todd Kuiken

  4. Institute of Biomedical Engineering, Northwestern University, Chicago, IL, USA

    Todd Kuiken

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  1. He Huang
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  2. Ping Zhou
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  4. Todd Kuiken
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Correspondence to Ping Zhou.

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Huang, H., Zhou, P., Li, G. et al. Spatial Filtering Improves EMG Classification Accuracy Following Targeted Muscle Reinnervation. Ann Biomed Eng 37, 1849–1857 (2009). https://doi.org/10.1007/s10439-009-9737-7

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  • DOI: https://doi.org/10.1007/s10439-009-9737-7

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