Temporal Generalization in fNIRS-Based Autism Classification: A Cross-Time-Window Transfer Benchmark

• URL: http://arxiv.org/abs/2608.07567v1
• Date: Mon, 03 Aug 2026 18:50:55 GMT
• Title: Temporal Generalization in fNIRS-Based Autism Classification: A Cross-Time-Window Transfer Benchmark
• Abstract summary: Functional near-infrared spectroscopy (fNIRS) is a promising modality for autism spectrum disorder (ASD) classification.<n>In practice, the optimal observation window varies across subjects due to differences in hemodynamic delay and neurovascular coupling.<n>We formalize this as a textitcross-time-window transfer problem, introducing a protocol that varies window length (2.5--10,s) and offset within biological motion trials.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Functional near-infrared spectroscopy (fNIRS) is a promising modality for autism spectrum disorder (ASD) classification, yet existing approaches assume temporally aligned evaluation. In practice, the optimal observation window varies across subjects due to differences in hemodynamic delay and neurovascular coupling, creating a temporal distribution shift that degrades performance. We formalize this as a \textit{cross-time-window transfer problem}, introducing a protocol that varies window length (2.5--10\,s) and offset within biological motion trials. Using topographic map representations of fNIRS recordings, we benchmark three vision architectures under two zero-shot baselines and eight adaptation strategies under leave-one-subject-out cross-validation ($N{=}124$). Key findings: (1) zero-shot cross-window accuracy is near chance (54--69\%); (2) ${\approx}5\%$ subject-specific fine-tuning recovers 90--96\%, while a subject-specific upper bound reaches 97--100\%, identifying inter-subject variability as the dominant barrier; (3) domain-adversarial and self-supervised strategies achieve 78--90\% without target-subject data; and (4) discriminative information is recoverable from windows as short as 2.5\,s. These findings provide a practical roadmap for deploying fNIRS-based ASD classifiers under realistic temporal variability.

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