Abstract
Brain–computer interface (BCI) is an emerging tool that has a variety of practical applications, including rehabilitation. BCIs are systems that extract and classify features in neural data, and then produce an output when a specific feature is detected. Motor imagery-based BCIs (MI BCIs), a more specific form of BCI, detect features that indicate the user is imagining a specific motor action, such as moving their arm or leg. There have been several studies released discussing the potential for BCIs to be used in a clinical setting for applications like rehabilitation. Spinal cord injuries (SCIs) are a form of injury that damages the spinal cord and causes either partial or total paralyzation. Those with SCI typically undergo rehabilitation for many years after the injury, and BCIs have begun to be tested for their benefits when included in SCI rehabilitation sessions. There are several ways for BCI systems to be used in SCI rehabilitation, which include virtual reality, exoskeletons, and neuroprosthesis. When using these methods as an output for a BCI system, SCI patients experience numerous benefits, most notably being an increase in mobility in the paralyzed region of their body. While there are several advantages to using BCIs for SCI rehabilitation, there are also several challenges that need to be addressed. In this chapter, we will discuss the current potential of BCIs for SCI rehabilitation, as well as what areas of this field need to be improved in the future.
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Merante, A., Zhang, Y., Kumar, S., Nam, C.S. (2020). Brain–Computer Interfaces for Spinal Cord Injury Rehabilitation. In: Nam, C. (eds) Neuroergonomics. Cognitive Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-34784-0_16
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