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ICPR
2010
IEEE

Recognizing Sign Language from Brain Imaging

10 years 6 months ago
Recognizing Sign Language from Brain Imaging
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.
Nishant Mehta, Thad Starner, Melody Moore Jackson,
Added 13 May 2010
Updated 13 May 2010
Type Conference
Year 2010
Where ICPR
Authors Nishant Mehta, Thad Starner, Melody Moore Jackson, Karolyn Babalola, George Andrew James
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