  Motor imagery (MI) is a well-documented technique used by subjects in BCI
(Brain Computer Interface) experiments to modulate brain activity within the
motor cortex and surrounding areas of the brain. In our term project, we
conducted an experiment in which the subjects were instructed to perform motor
imagery that would be divided into two classes (Right and Left). Experiments
were conducted with two different types of electrodes (Gel and POLiTag) and
data for individual subjects was collected. In this paper, we will apply
different machine learning (ML) methods to create a decoder based on offline
training data that uses evidence accumulation to predict a subject's intent
from their modulated brain signals in real-time.
