  This paper formulates a generalized classification algorithm with an
application to classifying (or `decoding') neural activity in the brain.
Medical doctors and researchers have long been interested in how brain activity
correlates to body movement. Experiments have been conducted on patients whom
are unable to move, in order to gain insight as to how thinking about movements
might generate discernable neural activity. Researchers are tasked with
determining which neurons are responsible for different imagined movements and
how the firing behavior changes, given neural firing data. For instance,
imagined movements may include wrist flexion, elbow extension, or closing the
hand. This is just one of many applications to data classification. Though this
article deals with an application in neuroscience, the generalized algorithm
proposed in this article has applications in scientific areas ranging from
neuroscience to acoustic and medical imaging.
