arXiv:2608. 07567v1 Announce Type: cross Abstract: Functional near-infrared spectroscopy (fNIRS) is a promising modality for autism spectrum disorder (ASD) classification, yet existing approaches assume temporally aligned evaluation.
arXiv:2607. 07957v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) affects over 75 million individuals worldwide, yet scalable computational methods for remote behavioral screening remain limited.
arXiv:2604. 16926v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations.
arXiv:2606. 16160v1 Announce Type: cross 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.
arXiv:2609.14159v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose early diagnosis remains challenging because conventional clinical assessments...
arXiv:2608.03176v2 Announce Type: replace Abstract: Imagined handwriting offers a temporally rich paradigm for non-invasive neural decoding, yet reliable recognition across unseen participants remain...
arXiv:2608. 06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal, multivariate, and even simple univariate medical time series.
AOI-Net introduces a structural face AOI-guided Eye‑Gaze Track Network that jointly models short‑term temporal dynamics and AOI‑level structural organization for Autism Spectrum Disorder detection. The network uses a gating mechanism to adaptively combine complementary representations and incorporates class‑distribution‑aware learning to address the imbalance between ASD and typically developing participants. Experiments on a large clinical eye‑tracking database with over 1,300 participants demonstrate that AOI‑Net outperforms state‑of‑the‑art methods and offers interpretable gaze‑behavior modeling for scalable AI‑driven ASD screening.
arXiv:2608.24727v1 Announce Type: cross Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especia...
BrainWideBench is a benchmark that evaluates across‑animal transfer on multi‑region neural recordings from 139 mice, covering 276 brain regions. It comprises three task suites—behavior decoding, neural activity prediction, and anatomical organization recovery—to test whether learned representations support diverse downstream objectives. The benchmark shows that while pretraining improves performance over single‑session baselines, current methods vary in transfer ability and none perform uniformly well across all suites, highlighting the challenge of developing general‑purpose neural representations.
X‑LMC is a spatiotemporal deep‑learning framework that automatically scores leptomeningeal collateral (LMC) status from time‑resolved biplane digital subtraction angiography (DSA). It uses a DINOv2 backbone to encode spatial frames, a token‑level cross‑view attention module to fuse orthogonal projections, and a recurrent network to model contrast bolus dynamics. On a multicenter dataset of 134 M1‑segment occlusion patients, X‑LMC achieved a Quadratic Weighted Kappa of 0.398 and a macro‑F1 of 0.711, outperforming static and other spatiotemporal baselines and matching clinical inter‑rater agreement.
EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tun...
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.