inEMG setup

Project

Intramuscular EMG Signal Processing

Summary

Body

Currently, surface electrodes are used to record the EMG signals—electrical signals produced when a muscle contracts—needed to control a myoelectric prosthesis. While surface electrodes are inexpensive and non-invasive, they are also prone to muscle cross talk—EMG signal contributions that originate from muscles but are not directly under the electrodes and electrode shift (movement of electrodes with respect to the skin surface during donning or use of the prosthesis). Cross talk and/or electrode shift often compromise the success of control algorithms. In contrast, intramuscular (implantable) EMG sensors may provide high-quality, cross talk–free measures of activation. Further, intramuscular EMG sensors enable researchers to attempt new myoelectric control strategies that may not be feasible with surface EMG.

Intramuscular EMG sensors:

• Allow the recording of muscles deep in the forearm, such as the supinator muscle, which would be nearly impossible using conventional recording methods.

• Prevent the shifting of electrodes during donning / doffing. Intramuscular EMG sensors may therefore provide a more stable interface and require less frequent training of the control system.

• Allow recording from inside the muscle, which opens up the skin surface for potential application of sensory feedback.

Our research objective is to develop new technologies for implanted EMG systems. Intramuscular EMG recordings will expand the set of algorithms that may be used to extract control information, improving the performance of the amplitude control algorithms and making configuration of the algorithms much easier. 

Related Publications

Body

Smith LH, Kuiken TA, and Hargrove LJ. Real-time simultaneous and proportional myoelectric control using intramuscular EMG. J Neural Eng, 11(6), 066013, 2014.

Young A, Smith L, Rouse E, and Hargrove L. Classification of Simultaneous Movements Using Surface EMG Pattern Recognition. IEEE T Biomed Eng, 60(5):1250-1258, 2013.

Li Y, Smith L, Hargrove L, Weber D, and Loeb G. Sparse Optimal Motor Estimation (SOME) for Extracting Commands for Prosthetic Limbs. IEEE Trans Neural Sys Rehab Eng, 21(1): 104-111, 2013.

Smith LH, Hargrove LJ, Lock BA, and Kuiken TA. Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay. IEEE Trans Neural Sys Rehab Eng, 19(2):186-92, Apr 2011.

Hargrove LJ, Englehart K and Hudgins B. A Comparison of Surface and Intramuscular Myoelectric Signal Classification. IEEE Transactions on Biomedical Engineering, 54(5), 847-853, 2007.

Save now, read later.