Fusion with language models improves spelling accuracy for ERP-based brain computer interface spellers

Umut Orhan, Deniz Erdogmus, Brian Roark, Shalini Purwar, Kenneth E. Hild, Barry Oken, Hooman Nezamfar, Melanie Fried-Oken

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

Event related potentials (ERP) corresponding to a stimulus in electroencephalography (EEG) can be used to detect the intent of a person for brain computer interfaces (BCI). This paradigm is widely utilized to build letter-by-letter text input systems using BCI. Nevertheless using a BCI-typewriter depending only on EEG responses will not be sufficiently accurate for single-trial operation in general, and existing systems utilize many-trial schemes to achieve accuracy at the cost of speed. Hence incorporation of a language model based prior or additional evidence is vital to improve accuracy and speed. In this paper, we study the effects of Bayesian fusion of an n-gram language model with a regularized discriminant analysis ERP detector for EEG-based BCIs. The letter classification accuracies are rigorously evaluated for varying language model orders as well as number of ERP-inducing trials. The results demonstrate that the language models contribute significantly to letter classification accuracy. Specifically, we find that a BCI-speller supported by a 4-gram language model may achieve the same performance using 3-trial ERP classification for the initial letters of the words and using single trial ERP classification for the subsequent ones. Overall, fusion of evidence from EEG and language models yields a significant opportunity to increase the word rate of a BCI based typing system.

Original languageEnglish (US)
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages5774-5777
Number of pages4
DOIs
StatePublished - 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

Keywords

  • Bayesian fusion
  • Brain computer interfaces
  • Event related potential
  • Language model

ASJC Scopus subject areas

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

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    Orhan, U., Erdogmus, D., Roark, B., Purwar, S., Hild, K. E., Oken, B., Nezamfar, H., & Fried-Oken, M. (2011). Fusion with language models improves spelling accuracy for ERP-based brain computer interface spellers. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 (pp. 5774-5777). [6091429] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/IEMBS.2011.6091429