Decomposition of surface EMG signals from cyclic dynamic contractions

Area of Science:

• Biomedical Engineering
• Neuroscience
• Kinesiology

Background:

• Electromyographic (EMG) signal decomposition into motor unit action potentials (MUAPs) is crucial for understanding muscle control.
• Existing algorithms are primarily limited to isometric contractions, failing to capture the complexity of dynamic movements.

Purpose of the Study:

• To develop and validate an algorithm capable of decomposing surface EMG (sEMG) signals during cyclic dynamic contractions.
• To investigate motor unit control strategies during dynamic movements and compare them to isometric conditions.

Main Methods:

• An established algorithm was incrementally enhanced using machine learning and time-varying MUAP shape discrimination.
• The algorithm was tested on sEMG data from pseudostatic and dynamic elbow flexion/extension and gait contractions.
• Decomposition accuracy was verified using two independent methods, achieving ~90% accuracy for motor unit firing instances.

Main Results:

• The enhanced algorithm successfully decomposed sEMG signals during complex dynamic contractions, including gait.
• High accuracy (~90%) in identifying motor unit firing instances and a substantial MUAP train yield (up to 25) were achieved.
• Motor unit control during dynamic contractions appears to follow the same principles as during isometric contractions.

Conclusions:

• The developed algorithm offers a robust solution for sEMG decomposition in dynamic movements.
• Common drive and hierarchical recruitment of motor units are preserved during both concentric and eccentric contractions.
• The findings challenge previous reports of modified motoneuron firing control properties during dynamic contractions.