Accurately classifying cognitive load from functional near-infrared spectroscopy (fNIRS) signals remains a significant challenge due to temporal variability, inter-subject differences, and sensitivity to preprocessing choices. This study provides a comprehensive evaluation of EEGNet for fNIRS-based cognitive load classification by systematically examining the effects of temporal segmentation strategies (overlapping vs. non-overlapping), window lengths (10s, 20s, 30s), feature extraction methods (Analysis of Variance (ANOVA), Principal Component Analysis (PCA), Fast Independent Component Analysis (FastICA)), learning rate configurations (fixed and adaptive), and evaluation protocols (random split vs. subject-independent (SI)). Results from random-split experiments show that overlapping segmentation, combined with smaller fixed learning rates (0.01-0.001), yields the highest accuracies, due to temporal redundancy and dense sampling of hemodynamic transitions. However, SI evaluation reveals a substantial drop in accuracy, demonstrating limited generalization to unseen participants. Under SI evaluation, non-overlapping segmentation outperformed overlapping windows, with the best accuracy of 56.11% achieved using PCA features with a 20-second window and a 0.1 learning rate. These findings indicate that eliminating temporal redundancy helps the model learn more robust and generalizable representations of cognitive load across individuals. Although adaptive learning rate strategy improved training stability, it did not surpass the performance of optimally selected fixed learning rates. The study highlights the critical role of segmentation strategy and learning rate selection in improving model generalization and identifies methodological considerations essential for developing reliable, real-time, and SI cognitive load classification systems using fNIRS.

翻译：准确对功能近红外光谱（fNIRS）信号中的认知负荷进行分类仍是一项重大挑战，原因在于时间变异性、个体间差异以及对预处理选择的敏感性。本研究通过对时间分段策略（重叠与非重叠）、窗口长度（10秒、20秒、30秒）、特征提取方法（方差分析（ANOVA）、主成分分析（PCA）、快速独立成分分析（FastICA））、学习率配置（固定与自适应）以及评估协议（随机拆分 vs. 受试者独立（SI））的系统性考察，对基于fNIRS认知负荷分类的EEGNet进行了全面评估。随机拆分实验结果表明，重叠分段结合较小的固定学习率（0.01-0.001）可达到最高准确率，这归因于时间冗余性和血流动力学变化密集采样的作用。然而，SI评估显示准确率大幅下降，表明模型对未见受试者的泛化能力有限。在SI评估下，非重叠分段的表现优于重叠窗口，其中使用PCA特征、20秒窗口和0.1学习率取得了56.11%的最佳准确率。这些发现表明，消除时间冗余有助于模型学习更鲁棒、更可泛化的个体间认知负荷表征。尽管自适应学习率策略提升了训练稳定性，但其性能并未超越最优选择的固定学习率。本研究强调了分段策略与学习率选择在提升模型泛化能力中的关键作用，并指出了开发基于fNIRS的可靠、实时且独立于受试者的认知负荷分类系统所必需的方法学考量。