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Functional Near-Infrared Spectroscopy for Reading Comprehension Analysis: A Feature-Based Study

In: Information Systems and Neuroscience

Author

Listed:
  • Ural Akincioglu

    (Karadeniz Technical University, Electronics and Communication Engineering Department, Faculty of Technology
    Medical Device and Production Application and Research Center)

  • Onder Aydemir

    (Karadeniz Technical University, Electrical and Electronics Engineering Department, Engineering Faculty
    Vitruvex Robotik Teknolojileri A.Ş
    Medical Device and Production Application and Research Center)

Abstract

This study explores the effectiveness of statistical feature extraction from functional near-infrared spectroscopy (fNIRS) signals in assessing English reading comprehension. Brain signals were recorded from 15 participants while reading 30 passages, followed by multiple-choice comprehension tests. Nine statistical features were extracted, and up to three-feature combinations were formed, resulting in 1,290 feature sets. The k-nearest neighbor (k-NN) classifier was utilized for classification, achieving an average accuracy of 73.48%. Among the statistical features, kurtosis was the most frequently selected, appearing 66 times, while skewness was the least selected, appearing 8 times. The highest and lowest classification accuracies were 76.30% and 71.85%, respectively. A unique dataset was collected by implementing an original experimental procedure in this study. This study contributes to the NeuroIS field by offering a novel, brain-based approach to evaluate reading comprehension, a key competency in multilingual information technologies teams and global digital collaboration environments.

Suggested Citation

  • Ural Akincioglu & Onder Aydemir, 2025. "Functional Near-Infrared Spectroscopy for Reading Comprehension Analysis: A Feature-Based Study," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 135-142, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-00815-2_13
    DOI: 10.1007/978-3-032-00815-2_13
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