EMG Feature Assessment for Myoelectric Pattern Recognition and Channel Selection: A Study with Incomplete Spinal Cord Injury

Med Eng Phys, 2014 · DOI: 10.1016/j.medengphy.2014.04.003 · Published: July 1, 2014

Spinal Cord Injury Assistive Technology Rehabilitation

Simple Explanation

This study introduces a strategy to reduce the number of channels used in high-density surface EMG recordings to develop a practical myoelectric control system. It minimizes channels by ranking the most discriminative features derived from all the EMG channels. The method was tested using 57 channels’ surface EMG signals recorded from forearm and hand muscles of individuals with incomplete spinal cord injury (SCI). The proposed strategy does not require repeatable implementation of the classification. Instead, it minimizes the number of channels by ranking the most discriminative features derived from all the EMG channels.

Study Duration

Not specified

Participants

9 subjects with incomplete cervical spinal injury (6 males, 3 females; age range 31–62 year; Neurological injury level C4–C8; ASIA class: C or D; Upper extremity motor score: 30–45)

Evidence Level

Not specified

Key Findings

1
Appropriate selection of a small number of raw EMG features from different recording channels resulted in similar high classification accuracies as achieved by using all the EMG channels or features.
2
Compared with the conventional sequential forward selection (SFS) method, the feature dependent method does not require repeated classifier implementation.
3
The results indicate that it is feasible to greatly reduce the number of raw EMG features or channels) while maintaining high classification accuracies achieved using high-density surface EMG.

Research Summary

This study presents a different strategy from the SFS method to reduce the number of channels used in high-density surface EMG recordings, toward developing a practical myoelectric control system. The feature dependent channel selection has two advantages over the SFS method. First, each iteration of the SFS method requires repeatedly training and testing of the classifier. The method can also be used for high-density EMG recordings from different populations. As an evaluation criterion, calculation of average overall classification accuracies was used to confirm the performance of the selected features or channels in this study.

Practical Implications

Improved Myoelectric Control

The feature-dependent channel reduction method can reduce computational cost for implementation of a myoelectric pattern recognition based control system.

Personalized Rehabilitation

Determination of appropriate number and location of EMG channels during implementation of a practical myoelectric control system (for neurological injury rehabilitation) should consider user difference.

Expanded Applications

The hybrid feature-channel selection method can be applied to other features, classifiers, and different populations, potentially improving rehabilitation outcomes.

Study Limitations

1
The robustness of the individual features (such as with respect to electrode shift, electrode size, orientation, etc) should also be considered for designing or implementing a practical myoelectric control system.
2
Channel selection is not only subject specific, but also related to different feature sets.
3
The evaluation criterion was calculation of average overall classification accuracies.

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