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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)

Abstract

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.

Keywords

Language Model Finite Impulse Response Rapid Serial Visual Presentation Simulation Mode Typing Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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