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Neural Responses to Abstract and Linguistic Stimuli with Variable Recognition Latency

  • Markus A. Wenzel
  • Carlos Moreira
  • Iulia-Alexandra Lungu
  • Mihail Bogojeski
  • Benjamin Blankertz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9359)

Abstract

Electroencephalography (EEG) can provide information about which words or items are relevant for a computer user. This implicit information is potentially useful for applications that adapt to the current interest of the individual user. EEG data were used to estimate whether a linguistic or abstract stimulus belonged to a target category that a person was looking for. The complex stimuli went beyond basic symbols commonly used in brain-computer interfacing and required a variable assessment duration or gaze shifts. Accordingly, neural processes related to recognition occurred with a variable latency after stimulus-onset. Decisions involving not only shapes but also semantic linguistic information could be well detected from the EEG data. Discriminative information could be extracted better if the EEG data were aligned to the response than to the stimulus-onset.

Keywords

EEG Single trial classification Physiological computing User relevance estimation 

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References

  1. 1.
    Picton, T.W.: The p300 wave of the human event-related potential. J. Clinical Neurophysiology 9(4), 456–479 (1992)CrossRefGoogle Scholar
  2. 2.
    ibbly - Pseudo-words. (acc: September 18, 2014) http://ibbly.com/Pseudo-words.html
  3. 3.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Friedman, J.H.: Regularized discriminant analysis. J. Am. Stat. Assoc. 84(405), 165 (1989)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Schäfer, J., Strimmer, K.: A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics. Statistical Applications in Genetics and Molecular Biology 4(1) (2005)Google Scholar
  6. 6.
    Blankertz, B., Lemm, S., Treder, M., Haufe, S., Müller, K.-R.: Single-trial analysis and classification of ERP components – a tutorial. NeuroImage 56(2), 814–825 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Markus A. Wenzel
    • 1
  • Carlos Moreira
    • 1
  • Iulia-Alexandra Lungu
    • 1
  • Mihail Bogojeski
    • 1
  • Benjamin Blankertz
    • 1
  1. 1.Neurotechnology GroupTechnische Universität BerlinBerlinGermany

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