Title:
Recognizing Sign Language from Brain Imaging
Recognizing Sign Language from Brain Imaging
| dc.contributor.author | Mehta, Nishant A. | |
| dc.contributor.author | Starner, Thad | |
| dc.contributor.author | Jackson, Melody Moore | |
| dc.contributor.author | Babalola, Karolyn O. | |
| dc.contributor.author | James, George Andrew | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Electrical and Computer Engineering | |
| dc.contributor.corporatename | Georgia Institute of Technology. Dept. of Biomedical Engineering | |
| dc.contributor.corporatename | Emory University. Dept. of Biomedical Engineering | |
| dc.contributor.corporatename | Georgia Institute of Technology. Biomedical Imaging Technology Center | |
| dc.contributor.corporatename | Emory University. Biomedical Imaging Technology Center | |
| dc.date.accessioned | 2010-01-28T20:52:00Z | |
| dc.date.available | 2010-01-28T20:52:00Z | |
| dc.date.issued | 2009 | |
| dc.description.abstract | 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. | en |
| dc.identifier.uri | http://hdl.handle.net/1853/31512 | |
| dc.language.iso | en_US | en |
| dc.publisher | Georgia Institute of Technology | en |
| dc.relation.ispartofseries | GVU Technical Report ; GIT-GVU-09-06 | en |
| dc.subject | Brain-computer interfaces | en |
| dc.subject | Functional magnetic resonance imaging (fMRI) | en |
| dc.subject | Movement impaired populations | en |
| dc.subject | Sign language | en |
| dc.title | Recognizing Sign Language from Brain Imaging | en |
| dc.type | Text | |
| dc.type.genre | Technical Report | |
| dspace.entity.type | Publication | |
| local.contributor.author | Starner, Thad | |
| local.contributor.author | Jackson, Melody Moore | |
| local.contributor.corporatename | GVU Center | |
| local.relation.ispartofseries | GVU Technical Report Series | |
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