arXiv AI

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

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.

By Marzieh Zare

rag fine-tuning benchmarks

arXiv AI

Jul 28

Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls

arXiv:2607. 24519v1 Announce Type: cross Abstract: Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear.

By Marzieh Zare

rag fine-tuning benchmarks

Hugging Face Trending Papers

Jul 27

Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls

Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear. We benchmark six models (LaBraM, EEGMamba, CBraMod, REVE, BENDR, and BIOT) on five clinical tasks across four datasets using frozen linear probes with leave-one-subject-out, subject-grouped, or explicitly identified recording-level splits.

rag fine-tuning benchmarks

arXiv Machine Learning

Aug 4

Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations

arXiv:2607. 24834v2 Announce Type: replace-cross Abstract: Objective.

By Marzieh Zare

rag

arXiv Machine Learning

Jun 8

The Identity Trap in EEG Foundation Models: A Diagnostic Audit

arXiv:2606. 06647v1 Announce Type: new Abstract: Objective.

By Jun-You Lin, Ying Choon Wu, Tzyy-Ping Jung

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 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 Machine Learning

Jul 9

STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning

arXiv:2607. 06629v1 Announce Type: new Abstract: Brain age -- the age inferred from a physiological recording -- is an emerging biomarker whose deviation from chronological age tracks neurological and psychiatric burden, and EEG is an attractive substrate for it because it is cheap, portable, and temporally rich.

By Roy Segal, Yoni Svechinsky, Tomer Fekete

llms rag nlp fine-tuning benchmarks

arXiv AI

5d ago

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding

arXiv:2608. 13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology.

By Shuailei Zhang, Muyun Jiang, Wei Zhang, Jinbo Chen, Zhiwei Guo, Yong Li, Yi Ding, Cuntai Guan

rag fine-tuning benchmarks

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

Jul 31

Evaluation Protocols and Cross-Subject Generalization in EEG Emotion Recognition

arXiv:2607. 27655v1 Announce Type: new Abstract: Reported accuracy in electroencephalography (EEG) emotion recognition depends on the complete evaluation procedure, not only the classifier.

By Hanting Suo, Yuwen Li

safety

arXiv Machine Learning

Jun 9

How Much Capacity Does EEG Denoising Need? Ultra-Compact Networks reveal Benchmark Saturation and Metric-Utility Gap

arXiv:2606. 08594v1 Announce Type: new Abstract: Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental variable or tested whether reconstruction metrics predict downstream neural-signal utility.

By Jasmeet Singh Bindra, Siddharth Panwar, Shubhajit Roy Chowdhury

llms efficiency benchmarks

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