2009,
Mehta, Nishant A.,
Starner, Thad,
Jackson, Melody Moore,
Babalola, Karolyn O.,
James, George Andrew
The problem of classifying complex motor activities from brain imaging is relatively
new territory within the fields of neuroscience and brain-computer interfaces.
We report positive sign language classification results using a tournament
of pairwise support vector machine classifiers for a set of 6 executed signs and
also for a set of 6 imagined signs. For a set of 3 contrasted pairs of signs, executed
sign and imagined sign classification accuracies were highly significant at 96.7%
and 73.3% respectively. Multiclass classification results also were highly significant
at 66.7% for executed sign and 50% for imagined sign. These results lay
the groundwork for a brain-computer interface based on imagined sign language,
with the potential to enable communication in the nearly 200,000 individuals that
develop progressive muscular diseases each year.