fNIR data classification using wavelet transforms and neural networks for attention monitoring

Alireza Akhbardeh, Meltem Izzetoglu, Scott Bunce, Kambiz Pourrezaei, Banu Onaral

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study investigates the potential of wavelet domain features on the classification of fNIR data for automated attention monitoring. Performance of two different neural classifiers is tested using wavelet based features and it is shown that fast and accurate classification of targets and non-targets can be reached.

Original languageEnglish (US)
Title of host publicationBiomedical Optics, BIOMED 2008
StatePublished - 2008
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

All Science Journal Classification (ASJC) codes

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

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  • Cite this

    Akhbardeh, A., Izzetoglu, M., Bunce, S., Pourrezaei, K., & Onaral, B. (2008). fNIR data classification using wavelet transforms and neural networks for attention monitoring. In Biomedical Optics, BIOMED 2008