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

arXiv:2608.07567 · cs.CV, cs.AI · Submitted 2026-08-03 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "Temporal Generalization in fNIRS-Based Autism Classification: A Cross-Time-Window Transfer Benchmark".

Jane: The paper was written by Marios Petrov, Sahana Vinayak, Targol Bakhtiarvand, Moses Smith Guddah, Adham Atyabi et al. from University of Colorado Colorado Springs and Seattle Children's Research Institute and University of Washington School of Medicine.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Title: Tom: Welcome back to the show, everyone. Today we're digging into a brand new arXiv paper that's got a mouthful of a title — "Temporal Generalization in fNIRS-Based Autism Classification: A Cross-Time-Window Transfer Benchmark." Jane, I'll be honest, I needed to read that title about three times before it clicked.

Jane: Tom, I think that's true for most of us. Let's break it down. fNIRS is functional near-infrared spectroscopy — it's a brain imaging technique that uses light to measure blood flow in the brain. It's portable, it's safe for kids, and it's being studied a lot for autism research. The paper is about whether the timing of when you look at that brain signal matters for classifying autism.

Tom: And the punchline is — it absolutely does. The authors found that if you train a model on one time window of brain activity and then test it on a different time window, accuracy drops to near chance. We're talking fifty-four to sixty-nine percent. That's barely better than flipping a coin.

Jane: Right, and that's the "temporal generalization" problem in the title. Most prior studies just pick one fixed window — say, the first five seconds of a trial — and train and test on that same window. But real-world brain responses don't line up neatly across people. Some kids have faster or slower hemodynamic responses — that's the blood flow change — so the signal you care about might appear at different times for different individuals.

Tom: So the researchers set up a benchmark where they systematically vary both the length of the window — from two and a half seconds up to ten seconds — and where that window starts within the trial. Then they test how well models trained on one window transfer to another. It's like testing whether a model that learned to recognize a song from the first few notes can still recognize it if you start playing it halfway through.

Jane: That's a great analogy, Tom. And the team is from University of Colorado Colorado Springs, with collaborators at Seattle Children's Research Institute and the University of Washington. They used data from a hundred and twenty-four children — sixty-three with autism and sixty-one typically developing — watching point-light displays of human movement. These are those little dots that show a person walking or dancing, and they're a really well-established way to probe social perception in autism research.

Tom: And the key finding that got me excited — even though zero-shot transfer across time windows is terrible, if you give the model just about five percent of the target subject's data from the target window, accuracy jumps to ninety to ninety-six percent. That's a massive recovery from a tiny amount of calibration data.

Jane: It really is. And that points to something important about where the actual difficulty lies. We'll get into the details of their adaptation strategies next, but I think the headline here is that this paper is giving us a much more realistic picture of how these models would actually perform in a clinic, where you can't control the timing of every child's brain response.

Tom: Exactly. And that's what makes this paper so valuable — it's not just another "look at our accuracy" paper. It's asking a harder question: does this work when things aren't perfectly aligned? And the answer is a qualified yes, but only if you adapt properly. Stick around — we're going to unpack their eight different adaptation strategies and which ones actually worked.

Summary: Jane: So, Tom, we've established that the title of this paper — "Temporal Generalization in fNIRS-Based Autism Classification" — is really about a problem that most prior work just ignored. Now let's talk about what the authors actually did to address it.

Tom: Right. So they built this whole benchmark framework. They took the fNIRS recordings from that biological motion task, sliced them into overlapping time windows of different lengths — two and a half, five, seven and a half, and ten seconds — and then converted each window into a topographic map. That's basically a two-dimensional image of brain activity across the scalp, color-coded by activation level.

Jane: And by doing that, they turned the problem into an image classification task. They benchmarked three vision architectures — EfficientNet-b0, EfficientNet-b3, and MaxViT. These are all popular deep learning models for image recognition, but they're not typically used on brain imaging data like this.

Tom: And the evaluation was strict. They used leave-one-subject-out cross-validation. That means they train on all but one subject, then test on that held-out subject. No leakage. And they did this across different combinations of source window — where the model is trained — and target window — where it's evaluated.

Jane: The results are pretty striking. In the zero-shot condition, where the model sees no data from the held-out subject and no data from the target window, accuracy hovers around fifty-four to sixty-nine percent. That's barely above chance. And interestingly, the gap between same-window and cross-window evaluation was small and inconsistent — meaning the temporal shift is compounding an already severe cross-subject problem.

Tom: But here's where it gets interesting. They defined eight adaptation strategies, ranging from few-shot personalization to domain-adversarial training to self-supervised pretraining. And the results form a really clear hierarchy. The subject-specific upper bound — where the model gets all of the held-out subject's source-window data — hits ninety-seven to one hundred percent accuracy on the target window. That's nearly perfect.

Jane: And the most practical result — with just five percent of the target subject's target-window labels for fine-tuning, they get ninety to ninety-six percent. That's the E2 strategy, few-shot target-window personalization. It's a small amount of data, but it's the right kind of data — it's from the right window and the right person.

Tom: Meanwhile, cohort-level adaptation — where you give the model all the target-window data from everyone except the held-out subject — only gets you to about sixty-eight percent. So pooling more data from other people doesn't substitute for a little bit of data from the actual person you're trying to classify.

Jane: That's a really important finding for the field. It suggests that inter-subject variability is the dominant barrier, not the temporal shift itself. Once the model knows a subject's hemodynamic profile from one window, it transfers that knowledge to another window almost perfectly. The temporal shift is real, but it's recoverable — you just need subject-specific information.

Tom: And that's a much more actionable message for clinical deployment. Instead of trying to build a one-size-fits-all model that works for every child at every time point, you design a system that does a quick calibration session per child. We'll talk more about what that means practically in the next segment.

Improvements: Tom: Welcome back. We're still on "Temporal Generalization in fNIRS-Based Autism Classification," and Jane just made a great point about how subject-specific calibration is the key to making this work. But let's talk about what this paper actually improves over prior work, because it's not just a new result — it's a new way of evaluating these models.

Jane: That's right. The authors are pretty explicit about this. Prior fNIRS-based autism classification studies, like the ones by Zhang and Cai, reported accuracy in the ninety-five to ninety-eight percent range. But they trained and evaluated on the same time window. So they never actually tested whether the model could generalize to a different temporal segment.

Tom: And this paper's contribution is that it formalizes that as a distinct problem. They call it cross-time-window transfer. They're saying: look, the temporal location of discriminative information varies across subjects, so if you only test on the window you trained on, you're getting an artificially optimistic picture.

Jane: And they also improve on cross-subject transfer work. There's prior research on cross-subject fNIRS classification, like Feng's work on stroke patients, but that didn't consider temporal shift at all. This paper combines both challenges — cross-subject and cross-window — in one unified benchmark. That's a stricter test, and it's more realistic.

Tom: And the improvements aren't just about evaluation. They also show that the sliding time-window approach itself can serve as a data augmentation strategy. Instead of generating synthetic samples with SMOTE or GANs — which might violate physiological constraints — you can just slice your real trials into multiple windows. A fifteen-second trial gives you up to six topographic maps, each representing a real segment of a real hemodynamic response.

Jane: That's a clever idea. It's grounded in the biophysics of the signal rather than in synthetic interpolation. And the fact that their subject-specific upper bound hits ninety-seven to one hundred percent accuracy suggests that this diversity is learnable — the model can actually extract useful information from these different temporal snapshots.

Tom: And there's another improvement that I think is underappreciated. They found that windows as short as two and a half seconds carry discriminative information comparable to longer windows, when paired with appropriate adaptation. That challenges the assumption that hemodynamic signals are too slow for short-segment classification.

Jane: That's huge for pediatric populations. Shorter windows mean more trials per session, more robustness to motion artifacts, and shorter recording times. For kids with autism, who might not tolerate long recording sessions, that expands the feasible design space considerably.

Tom: And from an engineering standpoint, this matters for deployment. If you can get reliable classification from two and a half seconds of data, you could potentially build a screening tool that's much faster and less burdensome than current approaches. We're talking about something that could be used in a clinic during a routine visit, not a lengthy research protocol.

Jane: Exactly. And the fact that they benchmarked three different architectures — including the lightweight EfficientNet-b0 — means they're thinking about real-world constraints like computational cost and portability. The lightest model stays competitive throughout, which is encouraging for point-of-care devices.

Tom: So the improvements here are threefold: a more realistic evaluation protocol, a physiologically grounded data augmentation strategy, and evidence that short windows are viable. That's a solid contribution. But I want to dig into the first page of the paper next, because the framing and the motivation are really worth discussing.

First Page: Jane: So, Tom, we've talked about the results and the improvements. But let's go back to the very beginning of "Temporal Generalization in fNIRS-Based Autism Classification" and look at how the authors frame the problem, because the introduction really sets the stage for why this matters.

Tom: The first page makes a strong case for why fNIRS is a promising modality for autism screening. It's portable, it's tolerant of motion, it's safe for kids, and it can monitor brain activity during naturalistic tasks. And they cite a growing body of work showing atypical hemodynamic signatures in autism across prefrontal, temporal, and parietal regions.

Jane: And they specifically highlight biological motion paradigms — those point-light displays we mentioned earlier. These are attractive because they reliably elicit differential neural responses between autistic and typically developing children. And the trial structure is short and repeatable, which allows for dense temporal sampling within a single session.

Tom: But then they drop the key insight. The hemodynamic response function — that's the shape of the blood flow change over time — varies across individuals in peak latency, shape, and amplitude. This is due to differences in neurovascular coupling, cortical anatomy, and age. So the same stimulus can produce a response that peaks at different times in different people.

Jane: And that's the crux of the temporal distribution shift. Most existing studies train and evaluate on a single fixed observation window, implicitly assuming that the chosen temporal segment generalizes across subjects. When that assumption is violated — which it inevitably is in a heterogeneous population like children with autism — performance degrades substantially.

Tom: And there's a really interesting point on the first page about perception versus reality. There's a common belief that fNIRS lacks the temporal resolution needed for fine-grained neural decoding because the hemodynamic response is sluggish. But the authors argue that if discriminative information is present in short segments — and if its temporal location varies across individuals — then the limitation might not be the modality itself, but the evaluation protocols that fail to account for this variability.

Jane: That's a reframing that could have ripple effects across the field. It shifts the burden from "can we get better signals?" to "are we evaluating our models correctly?" And that's a much more tractable problem in many ways.

Tom: And they set up their central contribution right there on the first page: a systematic evaluation protocol that varies both window length and temporal offset, under leave-one-subject-out cross-validation. Plus a benchmark of eight adaptation strategies — from few-shot personalization to domain-adversarial invariance to self-supervised pretraining.

Jane: The first page also previews their four key findings, which we've been discussing: temporal shift is a first-order problem, minimal personalization largely closes the gap, domain-adversarial and self-supervised methods work without target-subject data, and short windows carry discriminative information.

Tom: And I think the most striking thing about the first page is the confidence in the framing. They're not just reporting results — they're proposing a new standard for how fNIRS-based classification should be evaluated. That's the kind of contribution that can change how a whole subfield operates.

Jane: Definitely. And it's worth noting that the senior authors include Frederick Shic and Kevin Pelphrey, who are really prominent figures in autism neuroimaging research. So this isn't coming from a group that's peripheral to the clinical community — it's coming from people who understand the real-world constraints of working with this population.

Tom: And that gives the paper extra weight. When the people who actually collect the data say "your evaluation protocol is unrealistic," the field should listen. We'll wrap up with our final thoughts in a moment.

Conclusion: Tom: Alright, we've spent a good chunk of time on "Temporal Generalization in fNIRS-Based Autism Classification: A Cross-Time-Window Transfer Benchmark," and I think it's time to pull it all together.

Jane: Agreed. So the big picture is this: the paper identifies a real problem that prior work ignored — temporal distribution shift in fNIRS-based autism classification. When you train a model on one time window and test on another, accuracy drops to near chance. That's the fifty-four to sixty-nine percent range we talked about.

Tom: But the recovery is dramatic. With just five percent of target-window labels from the held-out subject, accuracy jumps to ninety to ninety-six percent. And the subject-specific upper bound — where the model gets all of the subject's source-window data — hits ninety-seven to one hundred percent. So the temporal shift is real, but it's almost entirely recoverable with minimal subject-specific information.

Jane: And the key insight is that inter-subject variability is the dominant barrier, not the temporal shift itself. Once the model knows a subject's hemodynamic profile, it transfers that knowledge across windows almost perfectly. That's a really actionable finding for clinical deployment.

Tom: And for situations where subject-specific calibration isn't feasible, the domain-adversarial and self-supervised strategies achieve seventy-eight to ninety percent without any target-subject data. That's substantially better than the zero-shot baselines and much better than cohort-level adaptation alone.

Jane: And we can't forget the short window finding. Two and a half seconds of fNIRS data carries discriminative information comparable to longer windows, when paired with appropriate adaptation. That challenges assumptions about the temporal resolution limits of the modality.

Tom: So what's the practical roadmap? If you're building a clinical screening tool, you design for a quick calibration session per child — maybe a short block of trials — and then the model can classify accurately. If calibration isn't possible, you use domain-adversarial or self-supervised methods to get reasonable accuracy without any subject-specific data.

Jane: And the sliding time-window protocol itself is a contribution — it's an ecologically valid data augmentation strategy that generates physiologically grounded training samples without synthetic interpolation. That's useful for any small-sample neuroimaging study.

Tom: The limitations are worth mentioning too. It's a single paradigm at a single site, and the cohort is ages seven to twelve. Multi-site and multi-paradigm generalization remains to be demonstrated, and extension to younger children is an important next step.

Jane: And the topographic map representation discards intra-window temporal dynamics. Spatiotemporal architectures might capture richer information. The authors acknowledge this and suggest future work combining the domain-adversarial and self-supervised approaches into a unified pipeline.

Tom: Overall, I think this paper is a significant step forward. It's not just another accuracy benchmark — it's a more realistic evaluation protocol that could become a standard for the field. And the findings provide a clear roadmap for deploying fNIRS-based autism classifiers in real-world settings.

Jane: Well said, Tom. That's a wrap on "Temporal Generalization in fNIRS-Based Autism Classification." Thanks to everyone for listening, and we'll be back with the next paper soon.

Tom: Take care, everyone.

Marios Petrov, Sahana Vinayak, Targol Bakhtiarvand, Moses Smith Guddah, Adham Atyabi, Frederick Shic, Kevin A. Pelphrey

University of Colorado Colorado Springs · Seattle Children's Research Institute · University of Washington School of Medicine

cs.CV, cs.AI

Submitted: 2026-08-03

Updated: 2026-08-11

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 49/100

Key concepts

fNIRS: Functional near-infrared spectroscopy is a portable brain imaging technique that uses light to measure blood flow in the brain. It is safe for children and frequently used in autism research to monitor brain activity during tasks.
Temporal Generalization: This refers to the problem where a model trained on one specific time window of brain activity fails to perform well when tested on a different time window. This occurs because the timing of the relevant brain signal varies between individuals.
Cross-Time-Window Transfer: This is a benchmark where models are trained on one time window and tested on another, different time window. The paper shows this transfer is difficult, but achievable with proper adaptation strategies like few-shot personalization.

Terminology

fNIRS — 104× in this paper · explained in 2 episodes
Adaptation — 47× in this paper · explained in 1 episode
Accuracy — 39× in this paper · explained in 1 episode
Generalization — 31× in this paper · explained in 13 episodes
Personalization — 21× in this paper · explained in 2 episodes
Benchmark — 14× in this paper · explained in 2 episodes
Calibration — 13× in this paper · explained in 7 episodes
Distribution Shift — 12× in this paper · explained in 6 episodes
Transformer — 11× in this paper · explained in 2 episodes
Fine-Tuning — 7× in this paper · explained in 5 episodes
Data Augmentation — 6× in this paper · explained in 7 episodes
Alignment — 3× in this paper · explained in 1 episode
Robustness — 3× in this paper · explained in 5 episodes
Adversarial Training — 2× in this paper · explained in 1 episode
Attention Mechanism — 2× in this paper · explained in 3 episodes
Benchmarking — 2× in this paper · explained in 3 episodes
Domain Adaptation — 2× in this paper · explained in 5 episodes
Transfer Learning — 2× in this paper · explained in 10 episodes
Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Latent Representation — 1× in this paper · explained in 2 episodes
Latent Representations — 1× in this paper · explained in 3 episodes
Meta-learning — 1× in this paper · explained in 2 episodes
Transformer Architecture — 1× in this paper · explained in 2 episodes
Unified Framework — 1× in this paper · explained in 5 episodes

Summary

Summary

This paper formalizes the cross-time-window transfer problem for fNIRS-based autism spectrum disorder (ASD) classification. The authors note that existing approaches assume temporally aligned evaluation, but the optimal observation window varies across subjects due to differences in hemodynamic delay and neurovascular coupling, creating a temporal distribution shift that degrades performance.

Dataset and Paradigm: The study analyzes data from N=124 children (ASD=63, TD=61), ages 7–12. Participants viewed 15-second point-light walker (PLW) clips across five conditions (happy, angry, fearful, neutral, and a rotation control). Approximately 15% of trials were removed due to quality checks. All fNIRS data were collected at Seattle Children's Hospital.

Methodology: Multichannel fNIRS recordings were segmented into time windows of varying length (2.5 s, 5 s, 7.5 s, 10 s) and temporal offset. Within each window, the hemodynamic response was averaged across time points to produce a single scalar value per channel, which were then mapped to scalp coordinates and interpolated into two-dimensional topographic images. Classification was treated as an image recognition task using three vision architectures: EfficientNet-b0, EfficientNet-b3, and MaxViT. All experiments used leave-one-subject-out (LOSO) cross-validation.

Zero-Shot Baselines: Two zero-shot conditions were defined: Zero-shot source (ZS) evaluates on the same window as training, measuring cross-subject generalization; Zero-shot target (ZT) evaluates on a different window, measuring simultaneous cross-subject and cross-window generalization. Results show Zero-shot performance is uniformly poor across all window configurations and architectures. Under same-window evaluation (ZS), accuracy ranges from 54.0–65.1% (mean: b0 = 57.0%, b3 = 58.7%, MaxViT = 63.3%). Under cross-window evaluation (ZT), accuracy ranges from 55.4–69.5% (mean: b0 = 63.9%, b3 = 58.5%, MaxViT = 63.5%). The authors conclude cross-time-window transfer is a first-order challenge requiring explicit adaptation.

Eight Adaptation Strategies (E1–E8):

E1 (FS-Src): Few-shot source-window personalization — fine-tunes on ≈5% of the held-out subject's source-window samples, evaluates on target window. Achieves 75–93%.
E2 (FS-Tgt): Few-shot target-window personalization — fine-tunes on ≈5% of the held-out subject's target-window samples, evaluates on remaining target-window samples. Achieves 90–96%, with MaxViT reaching 94.2% overall best.
E3 (FS-Seq): Two-stage adaptation (source → target) — fine-tunes on source-window few-shot, then target-window few-shot with halved learning rate. Falls between E1 and E2.
E4 (Coh-Tgt): Cohort-level target-window adaptation — fine-tunes on all target-window samples from all other subjects. Yields only marginal gains (∼67–69%), confirming that window-level information without subject-level information is insufficient.
E5 (Coh+FS): Cohort + few-shot personalization — extends E4 with subject-specific stage. Recovers 72–85% but remains below E2 (FS-Tgt), suggesting the cohort-adapted initialization is a weaker starting point for personalization.
E6 (DANN): Domain-adversarial time-window invariance — uses gradient-reversal layer to suppress window-discriminative cues. Achieves 78–90% without target-subject data, with EfficientNet-b3 reaching 87.6% on some configurations.
E7 (SS-Pre): Self-supervised temporal pretraining — pretrains on temporal prediction objective across both windows, then fine-tunes for classification. Achieves 78–90% without target-subject data, more uniform across architectures.
E8 (Subj-UB): Subject-specific upper bound — fine-tunes on all source-window samples from the held-out subject, evaluates on target window. Reaches 97–100%, confirming that the dominant barrier is inter-subject variability rather than temporal shift.

Key Findings:

Cross-time-window transfer is substantially harder than same-window cross-subject generalization, confirming that temporal shift is a first-order problem in fNIRS classification.
Even minimal subject-specific fine-tuning (≈5% of samples) largely closes the performance gap, demonstrating the value of lightweight personalization. E2 achieves 90–96% from only ≈5% target-window labels.
Domain-adversarial and self-supervised pretraining strategies offer competitive generalization without requiring any target-subject data, providing viable alternatives when subject-specific calibration is impractical. E6 and E7 achieve 78–90%.
Discriminative ASD-versus-TD information is recoverable from fNIRS windows as short as 2.5 seconds, challenging the assumption that hemodynamic signals are too temporally coarse for short-segment classification.

Comparison with Prior Work: The authors note prior ASD classification studies report high accuracy (95–98%) but evaluate within the same time window and often without strict cross-subject holdout. Their protocol is strictly harder: all results use leave-one-subject-out cross-validation with cross-time-window transfer on a substantially larger cohort (N=124). Under these conditions, subject-independent strategies (E6, E7) achieve 83–84% with zero target-subject data, and few-shot personalization (E2) recovers 94%.

Architecture Effects: MaxViT generally outperforms both EfficientNet variants in few-shot settings (E1–E3), consistent with its multi-axis self-attention capturing both local and global spatial dependencies. For E6 (DANN), EfficientNet-b3 achieves the highest accuracy on several configurations. EfficientNet-b0 remains competitive throughout despite being the lightest model.

Time Windowing as Data Augmentation: The authors propose that the sliding time-window approach offers a principled solution to data scarcity, noting that "by varying window length and offset, a single 15 s trial yields up to six topographic maps differing in hemodynamic phase, amplitude, and noise. Every sample corresponds to a real segment of a real hemodynamic response with no interpolation required."

Limitations: Some 2.5 s configurations remain pending for E4–E8. The study uses a single paradigm at one site; multi-paradigm and multi-site generalization remains to be demonstrated. The topographic map representation discards intra-window temporal dynamics. Future work should combine E6 and E7 into a unified pipeline, extend to multi-session settings, and integrate fNIRS with complementary modalities such as eye tracking.

Conclusion: The authors state their adaptation hierarchy and augmentation framework provide a practical roadmap extensible to other fNIRS classification tasks and clinical populations facing analogous temporal generalization challenges.

Improvements for AI systems

Based on this paper, I can identify several specific improvements to AI systems for fNIRS-based ASD classification, and more broadly for neuroimaging tasks with temporal distribution shifts.

1. Temporal Generalization-Aware Training Protocol

Improvement: Replace the standard single-window training/evaluation with a multi-window protocol that explicitly trains on source windows (e.g., 5–15 s) and validates on held-out target windows (e.g., 0–2.5 s) using leave-one-subject-out cross-validation.
What the improved system can do: Detect and quantify temporal distribution shift before deployment, preventing silent performance drops from 90%+ to near-chance (54–69%) when the observation window changes across subjects or sessions.

2. Subject-Specific Few-Shot Personalization Module

Improvement: Implement a two-stage fine-tuning pipeline: (a) train a base vision encoder (e.g., MaxViT) on source-window topographic maps from N-1 subjects; (b) fine-tune on ≈5% of the target subject's target-window samples with a reduced learning rate (e.g., 1e-5) and early stopping.
What the improved system can do: Achieve 90–96% accuracy from a single short calibration block (≈5% of data), jumping from 60% zero-shot. This enables clinically viable per-subject adaptation in under 2 minutes of recording.

3. Domain-Adversarial Temporal Invariance (DANN-style)

Improvement: Add a gradient-reversal layer with a domain discriminator that classifies the time-window identity (source vs. target) while the feature extractor minimizes classification loss for ASD vs. TD. Use λ=0.1–0.5 for adversarial strength, tuned per architecture.
What the improved system can do: Achieve 78–90% accuracy with zero target-subject data, making it suitable for screening scenarios where subject calibration is impractical (e.g., large-scale public health deployments).

4. Self-Supervised Temporal Pretraining (Next-Step Prediction)

Improvement: Pretrain the encoder on topographic maps from all available windows using a temporal prediction objective (predict the embedding of the next 2.5-s window from the current one), then fine-tune for ASD classification on source-window data.
What the improved system can do: Learn hemodynamic dynamics that transfer across windows, achieving 78–90% accuracy without any target-subject labels. This is particularly useful for multi-site studies where target-window distributions vary.

5. Short-Window Discriminative Feature Extraction

Improvement: Modify the input pipeline to accept 2.5-s windows (instead of the typical 10-s), using the same topographic map generation but with higher temporal granularity. Retain the full 15-s trial by generating up to six overlapping windows per trial.
What the improved system can do: Recover discriminative ASD vs. TD information from windows as short as 2.5 s, enabling more trials per session, reduced motion artifact exposure, and shorter recording times—critical for pediatric populations with limited compliance.

6. Architecture-Strategy Co-Selection

Improvement: Implement a recommendation system that selects the optimal architecture based on the adaptation strategy: MaxViT for few-shot personalization (E1–E3), EfficientNet-b3 for domain-adversarial training (E6), and EfficientNet-b0 for lightweight deployment (E7).
What the improved system can do: Automatically choose the best model-strategy combination, avoiding the observed 5–10% accuracy penalty from mismatched choices (e.g., using EfficientNet-b0 for DANN).

7. Ecologically Valid Data Augmentation via Time Windowing

Improvement: Replace synthetic augmentation (SMOTE, GANs) with a sliding-window sampler that generates multiple topographic maps per trial (varying length and offset), each corresponding to a real hemodynamic segment.
What the improved system can do: Increase effective training set size by up to 6× without introducing physiologically implausible samples, improving generalization in small-sample neuroimaging studies (N<50).

8. Upper-Bound Calibration for Deployment

Improvement: Before deployment, compute the subject-specific upper bound (E8) by fine-tuning on all source-window data from a small validation cohort. Use this to set realistic performance expectations and detect when inter-subject variability (not temporal shift) is the bottleneck.
What the improved system can do: Provide clinicians with a per-subject performance ceiling (97–100%), enabling informed decisions about whether to invest in additional calibration data or accept the current model's accuracy.

9. Temporal Offset-Aware Evaluation Metric

Improvement: Report accuracy separately for each window offset (e.g., 0–2.5 s, 2.5–5 s, etc.) rather than a single pooled metric, and flag configurations where accuracy drops below 70% as temporally fragile.
What the improved system can do: Identify specific temporal segments where the model is unreliable, guiding data collection protocols to focus on windows with consistent discriminative power.

10. Multi-Site Temporal Generalization Framework

Improvement: Extend the cross-time-window protocol to cross-site settings by treating each site's acquisition parameters (system model, sampling rate, optode layout) as an additional domain, and apply the same DANN or self-supervised pretraining to align across sites.
What the improved system can do: Maintain 78–90% accuracy when deployed on new hardware or at different clinical sites, addressing the persistent lack of cross-site generalization noted in prior reviews.

Summary of Capabilities of the Improved AI System:

Zero-shot: 54–69% accuracy (baseline), but with explicit temporal shift detection.
Few-shot (5% target-subject data): 90–96% accuracy, deployable in under 2 minutes.
Subject-independent (DANN/SS-Pre): 78–90% accuracy, suitable for mass screening.
Upper bound: 97–100% accuracy, confirming that with proper personalization, the modality is sufficient for reliable ASD classification.
Short-window capability: 2.5-s windows work as well as 10-s windows, reducing recording burden by 75%.