Improved accuracy using recursive bayesian estimation based language model fusion in ERP-based BCI typing systems.

U. Orhan, D. Erdogmus, B. Roark, Barry Oken, S. Purwar, K. E. Hild, A. Fowler, Melanie Fried-Oken

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

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.

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Brain-Computer Interfaces
Brain computer interface
Electroencephalography
Evoked Potentials
Language
Fusion reactions
Self-Help Devices
Quadriplegia
Bayes Theorem
Bioelectric potentials
Signal-To-Noise Ratio
Signal to noise ratio
Decision Making
Decision making
Communication

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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title = "Improved accuracy using recursive bayesian estimation based language model fusion in ERP-based BCI typing systems.",
abstract = "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 na{\"i}ve 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.",
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AU - Orhan, U.

AU - Erdogmus, D.

AU - Roark, B.

AU - Oken, Barry

AU - Purwar, S.

AU - Hild, K. E.

AU - Fowler, A.

AU - Fried-Oken, Melanie

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