Individuals affected by severe physical limitations, such as those caused by amyotrophic lateral sclerosis (ALS) or brainstem stroke, may not have the physical ability required to use clinically available augmentative and assistive communication systems. The P300 speller relies on the detection of responses elicited in EEG signals and has been used as a method of technology access for individuals with significant disability 1, 2. Our research focuses on improving P300 spellers in two areas: improved pattern recognition techniques and channel selection techniques for detecting P300 event-related potentials (ERPs) in the measured multi-channel EEG data, and optimal stimulus selection for improved efficiency and performance.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etsu-works-2-1934 |
| Date | 01 June 2010 |
| Creators | Throckmorton, Chandra S., Ryan, David B., Hanmer, B., Caves, C., Colwell, Kenneth, Sellers, Eric W., Collins, Leslie M. |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Detected Language | English |
| Type | text |
| Source | ETSU Faculty Works |
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