Brain-computer interfaces and their potential for restoring motor function in paralysis

Developed in the late 20th century, brain-computer interfaces (BCIs) have undergone advancements in machine learning, which have revolutionized research in the field of cognitive rehabilitation (Birbaumer, 2007). 

BCIs are systems that enable direct communication between the brain and a device, bypassing established neural pathways. In healthcare, the system translates the brain's commands to control external technologies (e.g. prosthetics) to minimize the impact of a traumatic brain injury (TBI)or assist individuals with disabilities. The system first detects brain signals, interprets them, and then converts the signals into commands with refined feedback. 

During paralysis and loss of motor function, a BCI can help patients reimagine movement via a system that provides feedback on their actions to promote neuroplasticity, the brain’s ability to form new neural connections, effectively correcting brain activity and patterns. 

In a 2006 experiment conducted by Neurochip, researchers determined that a BCI electrical simulation on monkeys triggered activity in the premotor cortex, a distant motor region (Gu, 2021). The study revealed the significance of BCIs in functional reorganization and how closed-loop systems and simulation can activate lost pathways.  

Currently, up to 30 percent of stroke patients facing severe paralysis are ineligible for traditional recovery programs like physical and movement therapy (Cervera, 2018). The rapid developments and studies on BCIs have the potential to transform the TBI recovery process, allowing even those who are completely paralyzed to work towards brain self-regulation and interaction. BCI technology bridges internal neural intentions with active external devices, offering new and promising technological support in patient rehabilitation. 

(ResearchGate, 2020)

References 

Birbaumer, N., & Cohen, L. G. (2007). Brain-computer interfaces: communication and

restoration of movement in paralysis. The Journal of Physiology, 579(Pt 3), 621–636. https://doi.org/10.1113/jphysiol.2006.125633

Cervera, M. A., Soekadar, S. R., Ushiba, J., Millán, J. D. R., Liu, M., Birbaumer, N., & Garipelli, G.

(2018). Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Annals of clinical and translational neurology, 5(5), 651–663. https://doi.org/10.1002/acn3.544

Gu, X., Cao, Z., Jolfaei, A., Xu, P., Wu, D., Jung, T.-P., & Lin, C.-T. (2021). EEG-based

brain–computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Advance online publication. https://doi.org/10.1109/TCBB.2021.3052811