Using fNIRS for Real-Time Cognitive Workload Assessment

  • Samuel W. HincksEmail author
  • Daniel Afergan
  • Robert J. K. Jacob
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)


In this paper, we evaluate the possibility of detecting continuous changes in the user’s cognitive workload using functional near-infrared spectroscopy (fNIRS). We dissect the source of meaning in a large collection of n-backs and argue that the problem of controlling the content of a participant’s mind poses a major problem for calibrating an algorithm using black box machine learning. We therefore suggest that the field simplify its task, and begin to focus on building algorithms that work on specialized subjects, before adapting these to a wider audience.


fNIRS Physiological interface Implicit interface Machine learning Default mode network Cognitive workload 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Samuel W. Hincks
    • 1
    Email author
  • Daniel Afergan
    • 2
  • Robert J. K. Jacob
    • 1
  1. 1.Tufts UniversityMedfordUSA
  2. 2.Google Inc.Mountain ViewUSA

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