title: Natural language processing with dynamic classification improves P300 speller accuracy and bit rate.
abstract: The P300 speller is an example of a brain-computer interface that can restore functionality to victims of neuromuscular disorders. Although the most common application of this system has been communicating language, the properties and constraints of the linguistic domain have not to date been exploited when decoding brain signals that pertain to language. We hypothesized that combining the standard stepwise linear discriminant analysis with a Naive Bayes classifier and a trigram language model would increase the speed and accuracy of typing with the P300 speller. With integration of natural language processing, we observed significant improvements in accuracy and 40-60% increases in bit rate for all six subjects in a pilot study. This study suggests that integrating information about the linguistic domain can significantly improve signal classification.
authors.affiliation: Biomedical Engineering Interdepartmental Program, University of California, Los Angeles, CA, USA. Speier@mii.ucla.edu
authors.forename: William
authors.initials: W
authors.lastname: Speier
authors.forename: Corey
authors.initials: C
authors.lastname: Arnold
authors.forename: Jessica
authors.initials: J
authors.lastname: Lu
authors.forename: Ricky K
authors.initials: RK
authors.lastname: Taira
authors.forename: Nader
authors.initials: N
authors.lastname: Pouratian
mesh_terms: D000328:Adult; D001931:Brain Mapping / Q000379:methods; D003143:Communication Devices for People with Disabilities; D004569:Electroencephalography / Q000379:methods; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D006801:Humans; D008297:Male; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D014584:User-Computer Interface; D014956:Writing