  Currently, strokes are the leading cause of adult disability in the United
States. Traditional treatment and rehabilitation options such as physical
therapy and tissue plasminogen activator are limited in their effectiveness and
ability to restore mobility and function to the patient. As a result, there
exists an opportunity to greatly improve the treatment for strokes. Machine
learning, specifically techniques that utilize Brain-Computer Interfaces (BCIs)
to help the patient either restore neurologic pathways or effectively
communicate with an electronic prosthetic, show promising results when applied
to both stroke diagnosis and rehabilitation. In this review, sources that
design and implement BCIs for treatment of stroke patients are evaluated and
categorized based on their successful applications for stroke diagnosis or
stroke rehabilitation. The various machine learning techniques and algorithms
that are addressed and combined with BCI technology show that the use of BCIs
for stroke treatment is a promising and rapidly expanding field.
