A comparative and critical study of EEGNet for fNIRS-driven cognitive load classification

2026-06-14

AI Summary

This study addresses the limited generalization performance in functional near-infrared spectroscopy (fNIRS)-based cognitive workload classification, which stems from temporal variability, inter-subject differences, and sensitivity to preprocessing choices. The authors systematically evaluate the efficacy of the EEGNet architecture for this task by comparing overlapping versus non-overlapping temporal segmentation, window lengths, feature extraction methods (ANOVA, PCA, FastICA), learning rate strategies (fixed vs. adaptive), and evaluation protocols (random split vs. subject-independent). They find that non-overlapping segmentation reduces temporal redundancy and significantly enhances cross-subject generalization. Combining PCA with a 20-second window length yields a subject-independent classification accuracy of 56.11%, establishing a new state-of-the-art result. The work underscores the critical influence of segmentation strategy and learning rate selection on model robustness.

Abstract

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.

Problem

• cognitive load classification
• fNIRS
• subject-independent
• temporal variability
• inter-subject differences

Innovation

• EEGNet
• fNIRS
• cognitive load classification
• subject-independent evaluation
• temporal segmentation