TY - GEN
T1 - Improved accuracy using recursive Bayesian estimation based language model fusion in ERP-based BCI typing systems
AU - Orhan, U.
AU - Erdogmus, D.
AU - Roark, B.
AU - Oken, B.
AU - Purwar, S.
AU - Hild, K. E.
AU - Fowler, A.
AU - Fried-Oken, M.
PY - 2012
Y1 - 2012
N2 - RSVP Keyboard™ is an electroencephalography (EEG) based brain computer interface (BCI) typing system, designed as an assistive technology for the communication needs of people with locked-in syndrome (LIS). It relies on rapid serial visual presentation (RSVP) and does not require precise eye gaze control. Existing BCI typing systems which uses event related potentials (ERP) in EEG suffer from low accuracy due to low signal-to-noise ratio. Henceforth, RSVP Keyboard™ utilizes a context based decision making via incorporating a language model, to improve the accuracy of letter decisions. To further improve the contributions of the language model, we propose recursive Bayesian estimation, which relies on non-committing string decisions, and conduct an offline analysis, which compares it with the existing naïve Bayesian fusion approach. The results indicate the superiority of the recursive Bayesian fusion and in the next generation of RSVP Keyboard™ we plan to incorporate this new approach.
AB - RSVP Keyboard™ is an electroencephalography (EEG) based brain computer interface (BCI) typing system, designed as an assistive technology for the communication needs of people with locked-in syndrome (LIS). It relies on rapid serial visual presentation (RSVP) and does not require precise eye gaze control. Existing BCI typing systems which uses event related potentials (ERP) in EEG suffer from low accuracy due to low signal-to-noise ratio. Henceforth, RSVP Keyboard™ utilizes a context based decision making via incorporating a language model, to improve the accuracy of letter decisions. To further improve the contributions of the language model, we propose recursive Bayesian estimation, which relies on non-committing string decisions, and conduct an offline analysis, which compares it with the existing naïve Bayesian fusion approach. The results indicate the superiority of the recursive Bayesian fusion and in the next generation of RSVP Keyboard™ we plan to incorporate this new approach.
UR - http://www.scopus.com/inward/record.url?scp=84883008937&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883008937&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2012.6346471
DO - 10.1109/EMBC.2012.6346471
M3 - Conference contribution
C2 - 23366432
AN - SCOPUS:84883008937
SN - 9781424441198
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2497
EP - 2500
BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Y2 - 28 August 2012 through 1 September 2012
ER -