The sensorimotor system, or the bodily process that links -physical movement to outside sensory information, is a key component of -human motion [@forbes_little_candow_2012]. -A healthy sensorimotor system is critical, as it is required to -communicate, interact, and navigate through one’s environment. Muscle -movement, a key component of motion and the motor system’s purpose, is -the carefully coordinated result between the nervous and muscular -systems.
-The Sensorimotor Physiology Lab at the UBC School of Kinesiology -studies the nervous system’s role in muscle movement. Dr. Jean-Sébastien -Blouin’s research is targeted at understanding the sensorimotor system’s -role in human balance, as well as expanding on the known -neurological-muscular mechanisms involved in human motion. By studying -the link between the nervous system and movement, researchers can -further understand the effects of neuromuscular damage. Findings can be -used to establish preventative measures, as well as to create more -effective therapeutic treatments for those with chronic neuromuscular -disorders and age-related neural degeneration [@purves_2018].
-The brain, spinal cord, and attached nerves form the nervous system, -which is responsible for initiating and propagating electrical movement -signals to the correct muscles. These electrical bursts, known as action -potential, are initially fired off from the brain. Action potential -propagates throughout the body via neurons, which are cells specialized -in transmitting electrical signals across long distances.
-Motor neurons are a specific subclass of neurons that create a -junction between the central nervous system and the muscular system. -Motor neurons attach to fibers in the muscle, forming an entity known as -a motor unit, as seen in figure @ref(fig:motor-unit). -When struck by action potential, the motor unit generates motor unit -action potential (MUAP), a burst of electrical activity specific to the -motor unit that produced it. The MUAP signal causes the muscle fibers -attached to the motor unit to contract [@purves_2018].
-(ref:motor-unit) Diagram of a single motor unit. Modified from @mclaughlin_2020.
-
--(ref:motor-unit) -
-The activation of several motor units (and their accompanying MUAPs) -are required to move a single muscle. Therefore, the number of motor -units involved in an isolated muscle movement can be determined by -measuring the net electrical charge of a single muscle.
-The partner uses non-invasive electromyography (EMG) to measure the -net firing of MUAPs across a single muscle, seen in figure -@ref(fig:emg-hardware)a as opposed to invasive EMG, seen -figure @ref(fig:emg-hardware)b. Surface electrodes are -placed on the skin and voltage is measured as participants flex and -relax a muscle. The partner uses a grid of 64 surface electrodes, as -seen in figure @ref(fig:EMG-ex), allowing the -collection of 64 streams of MUAP data across a single muscle. The raw -signal collected by EMG is the result of many MUAP peaks constructively -and destructively interfering with each other.
-(ref:emg-hardware) Obtaining EMG with a. surface electrodes and b. -needle electrodes. Invasive EMG using needle electrodes is the more -commonly used technique, as it allows access to deeper muscle groups and -higher muscle region selectivity which decreases noise. In comparison, -non-invasive EMG using surface electrodes are less selective and can -only sample superficial muscles, as they sit on the skin far away from -the muscle fibres [@emg_decomposition_tutorial]. Non-invasive EMG -is also a more flexible and easier-to-apply technique with higher -long-term stability than invasive EMG, which are desirable attributes to -the partner [@farina_holobar_2015]. Figure -modified from @nam_cha_park_2021.
-
--(ref:emg-hardware) -
-(ref:EMG-ex) Diagram of a single motor unit. Modified from @mclaughlin_2020.
-
--Diagram of surface EMG with 3 electrodes, and the 64 channel template -used by the Sensorimotor Physiology Lab. -
-A blind source separation algorithm can decompose raw EMG signals
-into several individual electrical signals that can be ascribed to
-singular muscle units. The partner currently decomposes EMG signals
-using a free software from @ot_bioelettronica called
-OTBioLab+. This software’s graphical user interface (GUI)
-can be seen in figure @ref(fig:OTBioelettronica-GUI).
-This software determines the individual MUAP spike trains using a
-closed-source algorithm based off a paper published by @negro_muceli_castronovo_holobar_farina_2016.
(ref:OTBioelettronica-GUI) ’Graphical user interface of the
-OTBioLab+ software [@ot_bioelettronica].
--(ref:OTBioelettronica-GUI) -
-The decomposition of EMG signals is a critical technique for the
-partner’s research. Decomposition allows researchers to gain a deeper
-understanding of which motor units are responsible for which parts of
-movement in a muscle. As is, OTBioLab+ is an unideal
-solution for decomposing EMG signals for the partner’s needs.
The creation of a custom Python package, EMGdecomPy, can
-address the issues with OTBioLab+, and contains custom
-functionalities specific to the partner’s use. OTBioLab+ is
-closed-source, which obscures how the partner’s experimental results are
-derived. By making EMGdecomPy open-source, the algorithm’s
-code can be easily inspected, circumventing this problem. Additionally,
-OTBioLab+ also has a 100 second time limit on experiments,
-which hinders the partner as they run experiments up to 5 minutes in
-length. In EMGdecomPy, there is no hard-coded time limit on
-experiments that can be run.
The transparency of EMGdecomPy’s code and the
-open-source licensing supports other parties expanding on and improving
-the existing functionalities. While made for the partner, it is our hope
-that EMGdecomPy will be a useful package for other
-researchers to explore the mechanisms underlying the brain’s
-interactions with the body’s muscles.