  Brain-Computer interfaces (BCI) are widely used in reading brain signals and
converting them into real-world motion. However, the signals produced from the
BCI are noisy and hard to analyze. This paper looks specifically towards
combining the BCI's latest technology with ultrasonic sensors to provide a
hands-free wheelchair that can efficiently navigate through crowded
environments. This combination provides safety and obstacle avoidance features
necessary for the BCI Navigation system to gain more confidence and operate the
wheelchair at a relatively higher velocity. A population of six human subjects
tested the BCI-controller and obstacle avoidance features. Subjects were able
to mentally control the destination of the wheelchair, by moving the target
from the starting position to a predefined position, in an average of 287.12
seconds and a standard deviation of 48.63 seconds after 10 minutes of training.
The wheelchair successfully avoided all obstacles placed by the subjects during
the test.
