Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients

• Voluntary surface EMG from neurological injury patients are often corrupted by involuntary background interference or spikes.
• We develop a novel framework to suppress involuntary motor activity contaminating voluntary surface EMG.
• The framework applies a Wiener filter based on tracking a priori SNR.
• The framework is characterized by quick and simple implementation.

Voluntary surface electromyogram (EMG) signals from neurological injury patients are often corrupted by involuntary background interference or spikes, imposing difficulties for myoelectric control. We present a novel framework to suppress involuntary background spikes during voluntary surface EMG recordings. The framework applies a Wiener filter to restore voluntary surface EMG signals based on tracking a priori signal to noise ratio (SNR) by using the decision-directed method. Semi-synthetic surface EMG signals contaminated by different levels of involuntary background spikes were constructed from a database of surface EMG recordings in a group of spinal cord injury subjects. After the processing, the onset detection of voluntary muscle activity was significantly improved against involuntary background spikes. The magnitude of voluntary surface EMG signals can also be reliably estimated for myoelectric control purpose. Compared with the previous sample entropy analysis for suppressing involuntary background spikes, the proposed framework is characterized by quick and simple implementation, making it more suitable for application in a myoelectric control system toward neurological injury rehabilitation.