(Georgia Institute of Technology, 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.