Abstract:
This paper presents a real-time Electroencephalogram
(EEG) classification system, with the goal of enhancing
the control of a head-movement controlled power wheelchair
for patients with chronic Spinal Cord Injury (SCI). Using a 32
channel recording device, mental command data was collected
from 10 participants. This data was used to classify three
different mental commands, to supplement the five commands
already available using head movement control. Of the 32 channels
that were recorded only 4 were used in the classification,
achieving an average classification rate of 82%. This paper
also demonstrates that there is an advantage to be gained by
doing adaptive training of the classifier. That is, customizing
the classifier to a person previously unseen by the classifier
caused their average recognition rates to improve from 52.5%
up to 77.5%.
