arXiv Machine Learning

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

Aug 19

OOD Detection for EEG-based Machine Learning in High-Risk Environments

The paper introduces a benchmark for out‑of‑distribution (OOD) detection in electroencephalography (EEG) machine learning, evaluates a wide range of OOD methods, and assesses their impact on two clinical downstream prediction tasks. It distinguishes between OOD detection and model uncertainty estimation, which are often conflated, and shows how combining complementary methods can create a robust safety net for deploying EEG‑based models in high‑risk settings.

By Philipp Bomatter, Henry Gouk

benchmarks safety

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.

By Targol Bakhtiarvand, Jugal Kalita, Adham Atyabi

llms rag computer-vision fine-tuning multimodal benchmarks safety

arXiv AI

Jun 19

Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU

arXiv:2606. 20074v1 Announce Type: cross Abstract: Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly during induced coma in Intensive Care Units (ICUs).

By Elisa Vasta, Thorir Mar Ingolfsson, Andrea Cossettini, Luca Benini, Tilman Beck, Emanuela Keller, Una Pale

fine-tuning benchmarks

arXiv Machine Learning

Jun 2

OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models

arXiv:2606. 00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions.

By Ziling Lu, Zongsheng Li, Xinke Shen, Kexin Lou, Yingyue Xin, Xiaoqi Chen, Shinan Wang, Xiang Chen, Jiahao Fan, Chenyu Huang, Xin Xu, Zhoujie Hou, Chen Wei, Quanying Liu

llms benchmarks

arXiv AI

Aug 10

ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?

arXiv:2608. 07033v1 Announce Type: new Abstract: This work investigates whether Electroencephalograph (EEG) foundation models (EFMs) can be made faster and locally deployable without sacrificing accuracy.

By Lingwei Li, Yirong Kan, Peng Chen, Xu Cao, Zheng Chen, Yasuhiko Nakashima

llms

arXiv AI

Jul 7

Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

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.

By Gabriel Jason Lee, Jathurshan Pradeepkumar, Jimeng Sun

benchmarks safety

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...

By Meghal Dani, Stefanie Liebe

fine-tuning benchmarks

arXiv Machine Learning

2d ago

Leakage-Safe Empirical Benchmarking of EEG-Based Machine Learning Pipelines for Dementia Classification

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...

By Haitian Wang, Chamara Madarasingha, Redowan Mahmud, Aneesh Krishna, Ryu Takechi

computer-vision multimodal benchmarks

arXiv Machine Learning

Jul 30

Detecting seizure onset and offset times using human intelligence: A critical-transitions-based approach

arXiv:2607. 27105v1 Announce Type: cross Abstract: Most existing seizure detection algorithms require extensive pre-processing of the data and rely on heuristic or currently unexplainable machine learning approaches.

By Andrew Flynn, Cian McCafferty, Klaus Lehnertz, Fran\c{c}ois David, Vincenzo Crunelli, Gordon Lightbody, Sebastian Wieczorek

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...

fine-tuning benchmarks

arXiv AI

Jun 2

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.

By Mufeng Chen, Qi Wu, Bingchao Huang, Xiwen Lai, Zekai Chen, Xinge Ouyang, Quansheng Ren

fine-tuning

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.

By Vigneshwar Hariharan (National University of Singapore), Chithra Reghuvaran (University College Dublin), Arlene John (University of Twente), Nhat Pham (Cardiff University), Omer Rana (Cardiff University), Deepu John (University College Dublin), Ganesh Neelakanta Iyer (National University of Singapore)

llms fine-tuning benchmarks

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