The Effect of Limiting Trial Count in Context Aware BCIs: A Case Study with Language Model Assisted Spelling

  • Mohammad MoghadamfalahiEmail author
  • Paula Gonzalez-Navarro
  • Murat Akcakaya
  • Umut Orhan
  • Deniz Erdogmus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9183)


Deflections in recorded electroencephalography (EEG) in response to visual, auditory or tactile stimuli have been popularly employed in non-invasive EEG based brain computer intefaces (BCIs) for intent detection. For example, in an externally stimulated typing BCI, an accurate estimate of the user intent might require long EEG data collection before the system can make a decision with a desired confidence. Long decision period can lead to slow typing and hence the user frustration. Therefore, there is a trade-off between the accuracy of inference and the typing speed. In this manuscript, using Monte-Carlo simulations, we assess the speed and accuracy of a Language Model (LM) assisted non-invasive EEG based typing BCI, RSVPKeyboard™, as a function of the maximum number of repetitions of visual stimuli sequences and the inter-trial interval (ITI) within the sequences. We show that the best typing performance with RSVPKeyboard™can be obtained when ITI=150 ms and maximum number of allowed sequences is 8. Even though the probabilistic fusion of the language model with the EEG evidence for joint inference allows the RSVPKeyboard™  to perform auto-typing when the system is confident enough t o make decisions before collecting EEG evidence, our experimental results show that RSVPKeyboard™does not benefit from auto-typing.


Language Model Finite Impulse Response Rapid Serial Visual Presentation Simulation Mode Typing Interface 
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  1. 1.
    Akcakaya, M., Peters, B., Moghadamfalahi, M., Mooney, A., Orhan, U., Oken, B., Erdogmus, D., Fried-Oken, M.: Noninvasive brain computer interfaces for augmentative and alternative communication. IEEE Rev. Biomed. Eng. 7(1), 31–49 (2014)CrossRefGoogle Scholar
  2. 2.
    Halder, S., Furdea, A., Varkuti, B., Sitaram, R., Rosenstiel, W., Birbaumer, N., Kübler, A.: Auditory standard oddball and visual p300 brain-computer interface performance. Int. J. Bioelectromag. 13(1), 5–6 (2011)Google Scholar
  3. 3.
    van der Waal, M., Severens, M., Geuze, J., Desain, P.: Introducing the tactile speller: an erp-based brain-computer interface for communication. J. Neural Eng. 9(4), 045002 (2012)CrossRefGoogle Scholar
  4. 4.
    Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510–523 (1988)CrossRefGoogle Scholar
  5. 5.
    Acqualagna, L., Blankertz, B.: Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP). Clin. Neurophysiol. 124(5), 901–908 (2013)CrossRefGoogle Scholar
  6. 6.
    Acqualagna, L., Treder, M.S., Schreuder, M., Blankertz, B.: A novel brain-computer interface based on the rapid serial visual presentation paradigm. In: Proceedings of Conference on IEEE Engineering in Medicine and Biological Society, vol. 1, pp. 2686–2689 (2010)Google Scholar
  7. 7.
    Orhan, U., Erdogmus, D., Roark, B., Oken, B., Purwar, S., Hild, K., Fowler, A., Fried-Oken, M.: Improved accuracy using recursive bayesian estimation based language model fusion in erp-based bci typing systems. In: Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC), vol. 2012, pp. 2497–2500. IEEE (2012)Google Scholar
  8. 8.
    Orhan, U., Erdogmus, D., Roark, B., Oken, B., Fried-Oken, M.: Offline analysis of context contribution to ERP-based typing BCI performance. J. Neural Eng. 10(6), 066003 (2013)CrossRefGoogle Scholar
  9. 9.
    Orhan, U., Hild, K. E., Erdogmus, D., Roark, B., Oken, B., Fried-Oken, M.: Rsvp keyboard: An eeg based typing interface. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 645–648. IEEE (2012)Google Scholar
  10. 10.
    Orhan, U., Akcakaya, M., Erdogmus, D., Roark, B., Moghadamfalahi, M., Fried-Oken, M.: Comparison of adaptive symbol presentation methods for rsvp keyboardGoogle Scholar
  11. 11.
    Sutton, S., Braren, M., Zubin, J., John, E.: Evoked-potential correlates of stimulus uncertainty. Science 150(3700), 1187–1188 (1965)CrossRefGoogle Scholar
  12. 12.
    Friedman, J.H.: Regularized discriminant analysis. J. Am. Stat. Assoc. 84(405), 165–175 (1989)CrossRefGoogle Scholar
  13. 13.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis, vol. 26. CRC Press, Boca Raton (1986)CrossRefzbMATHGoogle Scholar
  14. 14.
    Roark, B., Villiers, J. D., Gibbons, C., Fried-Oken, M.: Scanning methods and language modeling for binary switch typing. In: Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies. Association for Computational Linguistics, pp. 28–36 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mohammad Moghadamfalahi
    • 1
    Email author
  • Paula Gonzalez-Navarro
    • 1
  • Murat Akcakaya
    • 2
  • Umut Orhan
    • 3
  • Deniz Erdogmus
    • 1
  1. 1.Northeastern UniversityBostonUSA
  2. 2.University of PittsburghPittsburghUSA
  3. 3.Honeywell InternationalMorristownUSA

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