Akincioglu Ural, Aydemir Onder, Cil Ahmet, Baydere Muhammed
Electronics and Communication Engineering Department of Faculty of Technology, Karadeniz Technical University, Trabzon, Türkiye.
Electrical and Electronics Engineering Department, Engineering Faculty, Karadeniz Technical University, Trabzon, Türkiye.
PLoS One. 2025 Jul 23;20(7):e0326359. doi: 10.1371/journal.pone.0326359. eCollection 2025.
Accurate, rapid, and objective reading comprehension assessments, which are critical in both daily and educational lives, can be effectively conducted using brain signals. In this study, we proposed an improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and symbolic aggregate approximation (SAX)-based method for determining the whole text reading comprehension status in English using functional near-infrared spectroscopy (fNIRS) signals. A total of 450 trials were recorded from 15 healthy participants as they read English texts. To facilitate labeling, participants were asked to rate their comprehension of the text using self-assessment scores, followed by answering a multiple-choice question with four options that comprehensively covered the whole text's content. The proposed method consists of pre-processing, feature extraction, and classification stages. In the pre-processing stage, intrinsic mode functions of the signals were obtained using the ICEEMDAN algorithm. In the feature extraction stage, following the SAX algorithm, statistical features were calculated. The extracted features were classified using the k-NN classifier. The proposed method tested three different labeling strategies: first, labeling the trials according to the responses to multiple-choice questions; second, labeling the trials based on self-assessment scores; and third, labeling the trials using a double-validation labeling strategy based on the intersection sets of the first two strategies. For the three strategies, the k-NN classifier achieved mean classification accuracies of 74.67%, 66.37%, and 89.02%, respectively. The results indicated that the proposed method could assess whole-text reading comprehension status in English.
准确、快速且客观的阅读理解评估在日常生活和教育中都至关重要，而利用脑信号可以有效地进行此类评估。在本研究中，我们提出了一种基于改进的自适应噪声互补总体经验模态分解（ICEEMDAN）和符号聚合近似（SAX）的方法，用于使用功能近红外光谱（fNIRS）信号来确定英文全文的阅读理解状态。在15名健康参与者阅读英文文本时，共记录了450次试验。为便于标注，要求参与者使用自我评估分数对其对文本的理解程度进行评分，然后回答一个包含四个选项的多项选择题，这些选项全面涵盖了全文内容。所提出的方法包括预处理、特征提取和分类阶段。在预处理阶段，使用ICEEMDAN算法获得信号的本征模态函数。在特征提取阶段，按照SAX算法计算统计特征。使用k近邻（k-NN）分类器对提取的特征进行分类。所提出的方法测试了三种不同的标注策略：第一，根据对多项选择题的回答对试验进行标注；第二，基于自我评估分数对试验进行标注；第三，使用基于前两种策略交集的双重验证标注策略对试验进行标注。对于这三种策略，k-NN分类器的平均分类准确率分别达到了74.67%、66.37%和89.02%。结果表明，所提出的方法能够评估英文全文的阅读理解状态。
