FOCUS is a lightweight Mamba‑2-based model designed for EEG‑based binary cognitive workload classification. It encodes multi‑channel EEG with a fixed FFT covering the five canonical bands and uses depthwise‑separable convolution combined with a weight‑shared encoder‑bottleneck Mamba‑2 pair, resulting in only 0.04 million parameters and 0.11 GFLOPs. In a pilot study with 11 participants, FOCUS achieved 74.75 % accuracy and 0.08 ms GPU forward latency, with weight sharing improving accuracy by 4.55 percentage points without statistical significance.
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.
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.
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.
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.
ThinkNet is a validation‑controlled framework that uses train‑only normalization, evolutionary search guided by validation, and validation‑gated inference to build compact motor‑imagery EEG decoders that generalize to unseen subjects. In a fixed benchmark on the BCI Competition IV‑2a dataset, the best single model achieved 44.35 % accuracy with only 4.9 k parameters, while a compact ensemble of six families reached 43.31 % accuracy. Across a broader search, models with ≤25 k parameters outperformed larger ones after retraining, but validation reliability remains a bottleneck under subject shift.
EEG-HealthLLM introduces a hierarchical adaptation framework that unifies heterogeneous EEG health assessments using large language models. Task‑specific EEG adapters preserve unique spatio‑temporal dynamics, while a shared LoRA module in the LLM backbone captures common high‑level assessment semantics. The new EEG‑HealthBench benchmark reorganizes five EEG datasets into five health‑assessment tasks, and experiments show EEG‑HealthLLM achieves the highest average balanced accuracy and macro‑F1, improving performance while reducing adapter size by 80%.
arXiv:2508. 17742v3 Announce Type: replace-cross Abstract: Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific progress.
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:2609.22092v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a low-cost and non-invasive signal source for dementia screening, yet existing EEG-based studies remain difficult to...
arXiv:2610.33832v1 Announce Type: cross Abstract: Sustained attention is critical for academic performance, yet existing auditory support tools are static and not grounded in real-time neural evidenc...
NeuroAtlas is the largest EEG benchmark to date, comprising 42 datasets and 260,000 hours of clinical EEG data across epilepsy, sleep medicine, brain age estimation, and brain‑computer interfaces. The study evaluates foundation models (FMs) for EEG against supervised baselines and generic time‑series FMs, finding that EEG‑specific FMs do not consistently outperform generic ones. It also demonstrates that standard machine‑learning metrics are inadequate for clinical relevance, advocating for task‑specific measures such as event‑level decision quality, hypnogram features, and brain‑age gap.
The study investigates whether Low‑Rank Adaptation (LoRA) can adapt three pretrained EEG foundation models—LaBraM‑base, REVE‑base, and REVE‑large—for binary left‑ vs. right‑hand motor imagery decoding in stroke patients. Using subject‑wise five‑fold cross‑validation on the PhysioNet EEG Motor Movement/Imagery Dataset and a binary subset of the UET175 stroke dataset, LoRA significantly improved accuracy for LaBraM‑base (0.822) and REVE‑base (0.957) on the healthy cohort, but only REVE‑base LoRA achieved high performance (0.847±0.194) on stroke data, with a best mean accuracy of 0.952 in leave‑one‑subject‑out evaluation. The results demonstrate that healthy‑benchmark performance does not guarantee transfer to stroke EEG, highlighting the need for target‑domain adaptation and subject‑level assessment in rehabilitation BCIs.