Paralysis affects 1 in 50 Americans, or 5.4 million people. Different types of paralysis dictate the extremities that are affected, including quadriplegia or tetraplegia, when all four limbs are paralyzed. Quadriplegia (tetraplegia) indicates the arms, legs, trunk, and pelvic organs are all affected by a spinal cord injury. In other types of paralysis, there may be a singular limb or side of the body that is affected. Understanding how the muscle are affected and ways to innervate them has become popular. The merging of the fields of neuroscience and technology has allotted for new technologies like machine learning and brain machine interfacing.
The research done by Dr. Fuglevand on, Mitigation of excessive fatigue associated with functional electrical stimulation, addresses the advances we have made with technology in neuroscience and also highlights the areas where technology has a bit further to go. Functional electrical stimulation uses small electrical impulses to activate specific nerves and muscles (Buckmire, 2018). In Dr. Fuglevand’s research, FES is apart of the rehabilitation process for those dealing with muscle paralysis but can lead to rapid fatigue of that targeted muscle due to the high branching of the muscle making a singular electrode not efficient enough (Buckmire, 2018). Dr. Fuglevand found that the innervation of the muscle with multiple electrodes combats the issues associated with the single electrode. More of the muscle will be innervated with multiple electrodes.
MathWorks which is a news platform discussing all research in deep brain learning discusses Ian Burkhart, who lost sensation and movement from his bicep and below due to a car accident in 2010. The researchers at Ohio State University and Batelle were in the midst of experimenting with BCI. Brain Machine Interfacing is the product of the merging of neuroscience and technology. BCI allots for the connection of the neural activity to body movements. Using BCI for those with paralysis means, computational systems record neuronal messages and then send those messages to a device in a series of commands (MathWorks, 2020). The Ohio State and Battelle used a microchip, in the form of the link between the brain and the body. The creation of NeuroLife by Battelle assisted Burkhart in regaining movement of the fingers, hand, and wrist. There is an electrode sleeve located on Burkart’s hand that receives these messages from the brain to anticipate hand movement. In the near future there are companies like Neuralink that are working towards a singular chip implantation to then predict and facilitate body movements with just a thought, no additional sleeve necessary.
Brain Machine Interfacing also addresses the difficulties with sensation, although there is the capability to touch an object, the sensation portion was not intact yet. This is an issue with Functional Electrical Stimulation, the motion and touch is there but the sensation is missing. In Burkhart’s situation, with the sleeve he regained movement but with MATLAB researchers they were able to create an algorithm to pick up on sensory information (MathWorks, 2020). This device wrapped around Burkhart’s bicep and for the first time in years he had perception of his surroundings. He was soon able to pick up objects without having to look first.
The research completed by Dr. Fuglevand in Functional Electrical Stimulation shows the greatness of our technology. The multi electrode stimulation is the basis that machine learning and brain machine interfacing is operating off of. BMI and ML seem to be the larger stepping stone from FES in paralysis treatments. BMI involves multiple working parts that allow for a person to regain many of the facilities lost by paralysis. The link between the chip and the rest of the body is why tech companies like Neuralink, Alphabet and Meta are investing billions into brain machine interfacing and deep brain learning, they too see it is the future.
References
Buckmire, A., Arakeri, T., Reinhard, J., & Fuglevand, A. (2018). Mitigation of excessive fatigue associated
with functional electrical stimulation. Journal Of Neural Engineering, 15(6), 066004. doi:
10.1088/1741-2552/aade1c
Works, M. (2020). Reconnecting the Brain After Paralysis Using Machine Learning. Retrieved 15
December 2021, from https://medium.com/mathworks/reconnecting-the-brain-after-paralysis-using-machine-learning-1a134c622c5d
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