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Classification subject effects using changes in cerebral blood flow on the Stroop test.

Tomoyuki Hiroyasu1, Michihiro Fukuhara, Hisatake Yokouchi

  • 1Department of Life and Medical Sciences, Doshisha University, Japan. tomo@is.doshisha.ac.jp

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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Functional near-infrared spectroscopy (fNIRS) can differentiate healthy individuals based on task performance and fatigue levels. This research demonstrates fNIRS

Area of Science:

  • Neuroscience
  • Psychology
  • Medical Imaging

Background:

  • Functional near-infrared spectroscopy (fNIRS) measures cerebral blood flow changes.
  • Variations in blood flow patterns are observed in mental illness and can be influenced by environment and psychological state.
  • Individual differences in fNIRS data suggest potential for subject classification.

Purpose of the Study:

  • To investigate if fNIRS data differs between subject groups categorized by task performance and questionnaire scores.
  • To determine if task performance scores differ between subject groups categorized by fNIRS data.
  • To explore automatic subject classification using combined task performance and fNIRS data.

Main Methods:

  • Classifying subjects into groups based on questionnaire responses and task performance scores.

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  • Analyzing fNIRS data for differences between these established groups.
  • Classifying subjects into groups based on fNIRS data.
  • Analyzing task performance scores for differences between fNIRS-defined groups.

    • Main Results:

    • Significant differences in fNIRS data were found between groups classified by questionnaire responses (e.g., incorrect answers) and fatigue levels.
    • Significant differences in task performance scores were observed between groups classified by fNIRS data.
    • These findings support the feasibility of automatic subject grouping.

    Conclusions:

    • Subject classification based on task performance and fNIRS data is achievable.
    • Fatigue and questionnaire responses are key factors influencing fNIRS data in healthy subjects.
    • This approach can enhance the accuracy and application of fNIRS in research and diagnostics.