Pyemgpipeline: A Python Package for Electromyography Processing.

We have developed an electromyography (EMG) signal processing pipeline package called pyemgpipeline, which is suitable for both surface EMG and intramuscular EMG processing. pyemgpipeline implements internationally accepted EMG processing conventions and provides a high-level interface for ensuring user adherence to those conventions, in terms of (1) processing parameter values, (2) processing steps, and (3) processing step order. The international standards are from surface EMG for non-invasive assessment of muscles (SENIAM) (Stegeman & Hermens, 1999). The seven processing steps included in the package are DC offset removal, bandpass filtering, full wave rectification, linear envelope, end frame cutting, amplitude normalization, and segmentation. In sport tasks particularly, it has been observed that amplitudes greater that 100% maximum voluntary contraction (MVC) can be observed (Devaprakash et al., 2016). Therefore, we will be using amplitude normalization recommendations from Devaprakash et al. (2016) for amplitude normalization – this method guarantees a muscle will not exceed 100% MVC as all EMG trials from the experiment are used to identify maximal muscle activation. What is not included in the package is time event detection and time normalization as different laboratories are interested in different phases of gait and prefer to use either linear or nonlinear time normalization techniques.