arXiv:2406. 07147v3 Announce Type: replace-cross Abstract: This study employs cutting-edge wearable monitoring technology to conduct high-precision, high-temporal-resolution (1-second interval) cognitive load assessment on electroencephalogram (EEG) data from the FP1 channel and heart rate variability (HRV) data of secondary vocational students.
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:2605. 22774v3 Announce Type: replace-cross Abstract: Assessing cognitive load continuously and at low latency would help adaptive human-computer interaction, but it remains hard because labeled data are scarce and models generalize poorly across subjects.
The paper presents a modality‑agnostic, hierarchical Transformer framework for assessing cognitive workload using heterogeneous biosignals. In a pilot study, the authors evaluated all 31 combinations of five modalities (ECG, EDA, RESP, SpO₂, EEG) across three tasks (IQ, MATH, GAME) and found that EEG alone performed best, while adding more modalities did not consistently improve results. The full five‑modality model achieved the highest average accuracy (73.02% on IQ, 68.08% overall) and reduced model size by about 50% compared to late‑fusion approaches.
The paper introduces the Individual Conformal Coupling Monitor (ICCM), a lightweight pre‑inference tool that detects structural ambiguity—when physiological signals that appear plausible individually form a pattern poorly supported by a person’s non‑stress baseline—in wearable stress classifiers. On the WESAD dataset, a Random Forest achieves high mean accuracy but fails entirely for Subject 14 due to weakened cross‑signal coupling near stress onset. ICCM quantifies subject‑specific coupling divergence and can route data to classify, defer, or abstain, reducing false positives slightly and withholding some misclassified windows, though it does not fully correct the failure.
BRIDGE-EEG is an efficient multi‑task EEG classification pipeline that leverages self‑supervised pretraining while dramatically reducing model size. It maps heterogeneous EEG recordings to a unified 62‑channel time‑frequency representation, pretrains an SE‑ResNet18 teacher with SimCLR, and distills it into smaller SE‑ResNet8 and SE‑ResNet4 students. The compact models achieve accuracy comparable to or better than larger foundation models on abnormality detection and emotion recognition, and they consume up to three times less energy on edge devices, enabling deployment on wearable hardware.
arXiv:2607. 01795v1 Announce Type: cross Abstract: Monitoring cognitive load during online learning could help instructors identify content that learners find difficult, but remote settings remove the visual cues that support this judgement in a classroom.
arXiv:2607. 08007v1 Announce Type: cross Abstract: Galvanic skin response (GSR) is widely used for stress detection, but wrist-based GSR remains challenging because its absolute amplitude can differ substantially from laboratory-grade palmar measurements.
arXiv:2606. 02598v1 Announce Type: new Abstract: Accurate and generalizable estimation of cognitive workload from electroencephalography (EEG) is critical for human-centered and safety-critical systems.
Galvanic skin response (GSR) is widely used for stress detection, but wrist-based GSR remains challenging because its absolute amplitude can differ substantially from laboratory-grade palmar measurements. In this paper, we propose a unit-independent low-rate wrist GSR processing pipeline to extract the number of skin conductance responses per minute (nSCR/min) as a stress-related feature.
BioSync is a transformer-based model that fuses cardiac, neural, behavioral, and speech data from wearables and mobile devices into a continuous composite digital biomarker called the BioSync Index (BSI). The architecture uses multi-head self-attention on modality tokens and a linear branch for feature concatenation, inspired by latent-variable measurement theory. Evaluations on synthetic cohorts for cognitive decline and metabolic-autonomic conditions show BioSync achieving AUCs of 0.928 and 0.764 accuracy/F1 of 0.766, outperforming simple concatenation and other fusion strategies in most corruption scenarios.
arXiv:2606. 14960v1 Announce Type: new Abstract: This study investigates the application of machine learning models to predict exam outcomes using physiological data collected during examination sessions.
SleepFM-2 is a foundation model trained on 282,511 polysomnography recordings, covering over two million hours of multimodal sleep physiology. It outperforms its predecessor in disease prediction, sleep scoring, and event detection, and its representation improves performance across diverse tasks—including wearable sensing, subjective sleep reports, and transfer to other EEG modalities. When combined with age, sex, and BMI, the model meets stringent discrimination criteria for 215 EHR phenotypes, adding reproducible information beyond demographics for 155 of them.