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.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Nishant Mehta, Thad Starner, Melody Moore Jackson, Karolyn Babalola, George Andrew James
• Keywords: Brain-computer Interface, Computer Vision, ICPR 2010, Sign Language, Sign Language Classification