fnirs

EEGNet Study Reveals Key Limitations in fNIRS Cognitive Load Classification

A comprehensive study published on arXiv systematically evaluates EEGNet for classifying cognitive load from fNIRS signals. The research highlights critical challenges in generalization, achieving only 56.11% accuracy under subject-independent evaluation, and underscores the importance of segmentation strategy and learning rate selection.