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Experiences in reading detection with EEG signals

Published:22 March 2010Publication History

ABSTRACT

This short paper introduces the use of neurophysiological signals, electroencephalogram (EEG), as a technique for reading detection. Reading detection is a focused problem, yet with multiple facets and application relevance.

The paper refers to the system architecture that supports the reading detection experiments, describes the sample tools designed for the experiment and discusses the results.

The experiences demonstrate that the approach to reading detection is feasible. In addition, they highlight the technical and usability limitations of the approach, which are expected to be reduced both through system tuning and optimization, and through the technological evolution of the peripheral devices.

References

  1. Bizas, E., Simos, G., Stam, C. J., Arvanitis, S., Terzakis, D., Micheloyannis, S. EEG Correlates of Cerebral Engagement in Reading Tasks, Brain Topography, Vol. 12, 1999. doi: 10.1023/A:1023410227707Google ScholarGoogle Scholar
  2. Jung, J., Mainy, N., Kahane, P., Minotti, L., Hoffmann, D., Bertrand, O., Lachaux, J. The Neural Bases of Attentive Reading, Human Brain Mapping 29:1193--1206 (2008), doi: 10.1002/hbm.20454Google ScholarGoogle ScholarCross RefCross Ref
  3. MathWorks. http://www.mathworks.com, 2009.Google ScholarGoogle Scholar
  4. Oliveira, I., Grigore, O. and Guimarães, N. Reading detection based on electroencephalogram processing, Proceedings of the WSEAES 13th international conference on Computers, Rhodes, Greece, 2009 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Wolpaw, J. R., Brain Computer Interfaces for Communication and Control, ASSETS'07, Oct 2007, AZ, USA, doi:10.1016/S1388-2457(02)00057-3 Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Experiences in reading detection with EEG signals

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          cover image ACM Conferences
          SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing
          March 2010
          2712 pages
          ISBN:9781605586397
          DOI:10.1145/1774088

          Copyright © 2010 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 22 March 2010

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          Acceptance Rates

          SAC '10 Paper Acceptance Rate364of1,353submissions,27%Overall Acceptance Rate1,650of6,669submissions,25%

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