Real-time assessment of mental workload with near-infrared spectroscopy: Potential for human-computer interaction

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

Research output: Contribution to conferencePaper

Abstract

We have used machine learning techniques to analyze functional near-infrared spectroscopy (fNIRS) data from the brain of human subjects to classify different levels of mental workload. Preliminary results show potential for fNIRS in human-computer interaction research.

Original languageEnglish (US)
PagesBMD14
StatePublished - Dec 1 2008
Externally publishedYes
EventBiomedical Optics, BIOMED 2008 - St. Petersburg, FL, United States
Duration: Mar 16 2008Mar 19 2008

Other

OtherBiomedical Optics, BIOMED 2008
CountryUnited States
CitySt. Petersburg, FL
Period3/16/083/19/08

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biomaterials
  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

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  • Cite this

    Fantini, S., Sassaroli, A., Tong, Y., Hirshfield, L. M., Girouard, A., Solovey, E. T., & Jacob, R. J. K. (2008). Real-time assessment of mental workload with near-infrared spectroscopy: Potential for human-computer interaction. BMD14. Paper presented at Biomedical Optics, BIOMED 2008, St. Petersburg, FL, United States.