Karmakar Subashis, Pal Tandra, Koley Chiranjib
Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, West Bengal 713209 India.
Department of Electrical Engineering, National Institute of Technology Durgapur, Durgapur, West Bengal 713209 India.
Cogn Neurodyn. 2025 Dec;19(1):58. doi: 10.1007/s11571-025-10242-0. Epub 2025 Apr 4.
Technology integration in modern education has transformed traditional teaching-learning methods, but maintaining student attentiveness during computer-aided activities remains challenging. Neuroimaging advancements provide valuable insights into cognitive processes. This study measures cognitive load during computer-aided education. We have collected functional near-infrared spectroscopy (fNIRS) brain signals while subjects perform mental tasks and rest. Three datasets have been considered to evaluate the performance of the proposed model. The first two datasets are open-access, and we prepare the third dataset by collecting fNIRS brain signals from 14 healthy subjects. Two feature extraction techniques are proposed: manual and automatic based on wavelet scattering transform (WST). A one dimensional convolutional neural network (1D CNN) is also proposed to automatically extract features through feature engineering and classification. For comparison, four machine learning classifiers, linear discriminant analysis (LDA), Naive Bayes (NB), k-nearest neighbors (KNN) and support vector machine (SVM), are also considered. Classification performance is evaluated using accuracy, precision, recall and F1-score across all datasets. Computational cost, i.e., the CPU time and memory utilization for extracting the features and testing the classifiers, is also evaluated. The results suggest that when considering four classifiers across three datasets and comparing among the manual and the WST-based feature extraction methods, the average performance of 1D CNN is superior in terms of classification accuracy (1.16 times higher), precision (1.10 times higher), recall (1.10 times higher) and F1-score (1.09 times higher). However, the CPU time and memory utilization for 1D CNN are significantly higher, 10.09 and 14.70 times, respectively. In comparison to four state-of-the-art deep learning models, the proposed 1D CNN also shows best classification accuracy (92.99%). The analysis of the results shows that identifying cognitive load, SVM with Gaussian kernel function on WST based methods, provides satisfactory classification performance with significantly less CPU time and memory utilization.
现代教育中的技术整合改变了传统的教学方法，但在计算机辅助活动中保持学生的注意力仍然具有挑战性。神经成像技术的进步为认知过程提供了有价值的见解。本研究测量了计算机辅助教育期间的认知负荷。我们在受试者执行心理任务和休息时收集了功能性近红外光谱（fNIRS）脑信号。考虑了三个数据集来评估所提出模型的性能。前两个数据集是开放获取的，我们通过收集14名健康受试者的fNIRS脑信号来准备第三个数据集。提出了两种特征提取技术：基于小波散射变换（WST）的手动和自动技术。还提出了一维卷积神经网络（1D CNN），通过特征工程和分类自动提取特征。为了进行比较，还考虑了四种机器学习分类器，即线性判别分析（LDA）、朴素贝叶斯（NB）、k近邻（KNN）和支持向量机（SVM）。使用所有数据集的准确率、精确率、召回率和F1分数来评估分类性能。还评估了计算成本，即提取特征和测试分类器的CPU时间和内存利用率。结果表明，在考虑三个数据集上的四个分类器并在基于手动和基于WST的特征提取方法之间进行比较时，1D CNN的平均性能在分类准确率（高1.16倍）、精确率（高1.10倍）、召回率（高1.10倍）和F1分数（高1.09倍）方面更优。然而，1D CNN的CPU时间和内存利用率分别显著更高，分别为10.09倍和14.70倍。与四种最新的深度学习模型相比，所提出的1D CNN也显示出最佳的分类准确率（92.99%）。结果分析表明，识别认知负荷时，基于WST方法的具有高斯核函数的SVM在显著更少的CPU时间和内存利用率下提供了令人满意的分类性能。
