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

Ural Akincioglu () and Onder Aydemir ()
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Ural Akincioglu: Karadeniz Technical University, Electronics and Communication Engineering Department, Faculty of Technology
Onder Aydemir: Karadeniz Technical University, Electrical and Electronics Engineering Department, Engineering Faculty

A chapter in Information Systems and Neuroscience, 2025, pp 135-142 from Springer

Abstract: 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.

Keywords: Functional near-infrared spectroscopy; Machine learning; Reading comprehension (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-00815-2_13

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DOI: 10.1007/978-3-032-00815-2_13

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