Discrimination of mental workload levels in human subjects with functional near-infrared spectroscopy

Angelo Sassaroli, Feng Zheng, Leanne M. Hirshfield, Audrey Girouard, Erin Treacy Solovey, Robert J.K. Jacob, Sergio Fantini

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


We have applied functional near-infrared spectroscopy (fNIRS) to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex. We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks. To quantify the potential of fNIRS for mental workload discrimination, we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) concentration changes associated with the working memory tasks. We have found classification success rates in the range of 4472%, which are significantly higher than the corresponding chance level (for random data) of 19.1%. This work shows the potential of fNIRS for mental workload classification, especially when more parameters (rather than just the amplitude of concentration changes used here) and more sophisticated classification algorithms (rather than the simple 3-nearest neighbor algorithm used here) are considered and optimized for this application.

Original languageEnglish (US)
Pages (from-to)227-237
Number of pages11
JournalJournal of Innovative Optical Health Sciences
Issue number2
StatePublished - Oct 2008
Externally publishedYes


  • Diffuse optical imaging
  • functional brain imaging
  • mental workload
  • near-infrared spectroscopy
  • working memory

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Medicine (miscellaneous)
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering


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