A non-parametric approach to the overall estimate of cognitive load using NIRS time series

Soheil Keshmiri*, Hidenobu Sumioka, Ryuji Yamazaki Skov, Hiroshi Ishiguro


研究成果: Article査読

15 被引用数 (Scopus)


We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females.

ジャーナルFrontiers in Human Neuroscience
出版ステータスPublished - 2017 2月 3

ASJC Scopus subject areas

  • 神経心理学および生理心理学
  • 神経学
  • 精神医学および精神衛生
  • 生物学的精神医学
  • 行動神経科学


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