Neurophysiological Measures of Brain Activity: Going from the Scalp to the Brain

  • Phan Luu
  • Catherine Poulsen
  • Don M. Tucker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)


Behavior, such as reaction time and correctness of a response, is the most studied output of the mind in the fields of psychology and human factors. With the advent of modern neuroimaging technologies, opportunities exist for direct study of the mind’s machinery: the brain. Moreover, there are opportunities for applying these technologies to solve a host of educational and engineering challenges, such as how to design better interfaces with computer systems or how to better educate and train students. The electroencephalogram (EEG) is a direct reflection of the functioning brain, and technologies that enable recording of the EEG have been in existence for more than 50 years. Within the past decade substantial progress has been made in EEG technology, permitting a direct view into the brain. We cover these advances in this paper, which include dense-sensor array technology and physics-based computational head models, and present several examples of how they have been applied.


Dense-Array EEG neuroergonomic 


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  1. 1.
    Posner, M.I.: Chronometric explorations of mind. Lawrence Earlbaum Associates (1978)Google Scholar
  2. 2.
    Koles, Z.J.: Trends in EEG source localization. Electroencephalography and clinical Neurophysiology, 127–137 (1998)Google Scholar
  3. 3.
    Srinivasan, R., Tucker, D.M., Murias, M.: Estimating the Spatial Nyquist of the Human EEG. Behav. Res. Meth. Inst., Comput. 30, 8–19 (1998)CrossRefGoogle Scholar
  4. 4.
    Luu, P., Shane, M., Pratt, N.L., Tucker, D.M.: Corticolimbic Mechanisms in the Control of Trial and Error Learning. Brain Res. 1247, 100–113 (2009)CrossRefPubMedGoogle Scholar
  5. 5.
    Luu, P., Tucker, D.M., Stripling, R.: Neural Mechanisms for Learning Actions in Context. Brain Res. 1179, 89–105 (2007)CrossRefPubMedGoogle Scholar
  6. 6.
    Chein, J.M., Schneider, W.: Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain general control network for learning. Cog. Brain Res. 25, 607–623 (2005)CrossRefGoogle Scholar
  7. 7.
    Debener, S., Ullsperger, M., Siegel, M., Fiehler, K., Yves von Cramon, D., Engel, A.K.: Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamic of performance monitoring. J. Neurosci. 25, 11730–11737 (2005)CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Phan Luu
    • 1
  • Catherine Poulsen
    • 1
  • Don M. Tucker
    • 1
  1. 1.Electrical Geodesics, Inc.EugeneUSA

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