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Published on: September 28, 2019
Spatial filtering improves EMG classification accuracy following targeted muscle reinnervation
He Huang1, Ping Zhou, Guanglin Li
1Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, 345 E. Superior Street, Suite 1406, Chicago, IL 60611, USA.
Annals of Biomedical Engineering
|June 16, 2009
Summary
High-density surface electromyography (EMG) with double differential filters improves targeted muscle reinnervation (TMR) control for prosthetic arms. This method enhances movement intent classification accuracy, particularly for hand movements, in amputees.
Area of Science:
- Biomedical Engineering
- Neuroprosthetics
- Rehabilitation Engineering
Background:
- Targeted muscle reinnervation (TMR) and electromyography (EMG) pattern classification are crucial for advanced myoelectric prostheses.
- High spatial resolution EMG recordings may capture focal muscle activity for better control.
Purpose of the Study:
- To investigate if high spatial resolution surface EMG improves movement intent classification accuracy in TMR subjects.
- To compare different spatial filtering techniques for EMG signal processing.
Main Methods:
- Recruited TMR subjects with transhumeral or shoulder disarticulations.
- Applied various spatial filters (single, double differential, 2D, high-order) to surface EMG recordings.
- Calculated classification accuracies for fifteen distinct movements.
Main Results:
- Spatially localized EMG signals, especially double differential filters, increased movement intent classification accuracy compared to monopolar recordings.
- Double differential filters yielded 5-15% higher accuracies than lower spatial resolution filters with 12 EMG signals.
- High spatial resolution filters showed comparable accuracies to double differential filters.
Conclusions:
- Double differential EMG recordings enhance the TMR-based neural interface for prosthetic arm control.
- This approach offers a robust method for multifunctional control of artificial arms.
- Optimizing spatial filtering of EMG signals is key for improving neuroprosthetic performance.
