arXiv AI
Jul 24
Multimodal Pretraining for Generalizable EEG Representation Learning
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.
CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention
arXiv:2606. 00074v1 Announce Type: cross Abstract: Reliable seizure prediction is a prerequisite for closed-loop neurostimulation therapy, yet existing methods rarely account for the variability in EEG signal quality encountered in real-world deployment, and the overwhelming majority adopt non-strict evaluation protocols that overestimate generalisation performance.
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arXiv Machine Learning
Aug 4
What EEG Foundation Models Encode: Dataset Identity and a Negative-Control Suite for Clinical Benchmarks
arXiv:2607. 24519v2 Announce Type: replace Abstract: Pretrained EEG foundation models are proposed for clinical decoding, but whether reported gains transfer across populations or survive negative controls is unclear.
arXiv AI
Aug 26
Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets
arXiv:2608.24727v1 Announce Type: cross Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especia...
arXiv AI
3d ago
NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
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.
arXiv AI
Aug 17
A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testing
arXiv:2607. 24519v3 Announce Type: replace-cross Abstract: EEG foundation-model gains may depend on cohort, montage, or probe design.
arXiv Machine Learning
Sep 2
Lightweight Adaptation of EEG Foundation Models for Stroke Motor Imagery Decoding: Domain Shift and Subject-Level Robustness
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.
arXiv Machine Learning
4d ago
Hybrid Ensemble Learning for EEG-Based Epileptic Seizure Forecasting
arXiv:2609.35876v1 Announce Type: new Abstract: Epileptic seizure forecasting aims to provide actionable warnings before seizure onset, yet patient-independent generalization and false-alarm control...
arXiv Machine Learning
Sep 4
RobustSeiz: An Open-Source Framework for Benchmarking the Robustness of EEG Seizure Detection Models
RobustSeiz is an open‑source, model‑agnostic framework designed to benchmark the robustness of EEG seizure detection models under realistic clinical stressors. It standardizes four public scalp‑EEG corpora into BIDS‑EEG trees, applies controlled distribution shifts—including environmental, noise, and adversarial transforms—across predefined hyperparameter grids, and reports comprehensive performance metrics such as sensitivity, precision, F1, false positives per 24 h, onset timing, and predictive agreement. The framework offers a Dockerized GPU pipeline, experiment registry, and both full‑evaluation and research‑subset modes, and demonstrates its utility by evaluating a contemporary detector on TUSZ across the full shift grid.
arXiv Machine Learning
Sep 22
Attention-Enhanced Dual-Branch ConvNeXt-BiLSTM Network for Subject-Independent EEG Seizure Detection
arXiv:2609.22141v1 Announce Type: cross Abstract: Automated seizure detection from scalp electroencephalography (EEG) is difficult because seizure morphology varies among patients and seizure samples...
arXiv AI
Jun 8
LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling
arXiv:2603. 19100v2 Announce Type: replace Abstract: Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains challenging due to differing electrode topologies and computational scalability, as Transformer architectures incur quadratic sequence complexity.
Hugging Face Trending Papers
Aug 25
Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets
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 Machine Learning
Jun 2
EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction
arXiv:2606. 02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.