Temporal Generalization in fNIRS-Based Autism Classification: A Cross-Time-Window Transfer Benchmark
This study addresses the challenge of cross-temporal-window distribution shift in functional near-infrared spectroscopy (fNIRS)-based autism classification, caused by inter-individual variability in hemodynamic response delays. The work formalizes this temporal transfer problem for the first time and establishes a benchmark encompassing varying window lengths and shifts. Leveraging fNIRS topographic maps processed through vision-based models, the authors systematically evaluate eight strategies—including zero-shot inference, adversarial domain adaptation, and self-supervised learning—under settings with no target data or minimal fine-tuning. Results reveal that zero-shot performance remains limited (54–69%), underscoring individual variability as a critical bottleneck. However, fine-tuning on merely ~5% of target subjects restores accuracy to 90–96%, while unsupervised domain adaptation achieves 78–90%, demonstrating that even short 2.5-second windows retain sufficient discriminative information.