arXiv Machine Learning

NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication

arXiv:2606. 20673v2 Announce Type: replace Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained.

arXiv Machine Learning

Aug 4

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models

arXiv:2601. 17883v3 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings.

By Dingkun Liu, Yuheng Chen, Zhu Chen, Zhenyao Cui, Yaozhi Wen, Jiayu An, Jingwei Luo, Dongrui Wu

fine-tuning benchmarks

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 Machine Learning

Aug 4

SingLEM: Single-Channel Large EEG Model

arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.

By Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue, Shoya Murakami, Kazi Mahmudul Hassan, Seungwoo Han, Ingon Chanpornpakdi, Toshihisa Tanaka

llms

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 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

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

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 Machine Learning

Aug 6

STEAM: A Spatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding

arXiv:2608. 02070v2 Announce Type: replace-cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.

By Zhu Chen, Dingkun Liu, Yuheng Chen, Dongrui Wu

safety

arXiv AI

Jul 24

MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning

arXiv:2607. 21402v1 Announce Type: new Abstract: Self-supervised foundation models have recently shown strong potential for electroencephalogram (EEG)-based analysis.

By Tao Zhou, Jing Han, Lingyu Shu, Zixing Zhang

nlp efficiency benchmarks

arXiv Machine Learning

Aug 4

STEAM:ASpatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding

arXiv:2608. 02070v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.

By Zhu Chen, Dingkun Liu, Yuheng Chen, Dongrui Wu

safety

arXiv Machine Learning

Jul 7

Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding

arXiv:2607. 03094v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding for brain-computer interfaces (BCIs) faces a major challenge: substantial inter-subject variability limits effective cross-subject generalization.

By Aymen Sarhane, Fouad Lbakali, Mouad Souissi, Jonathan Lys, Giulia Lioi

fine-tuning benchmarks

arXiv Machine Learning

23h ago

SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models

arXiv:2608. 16931v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for EEG decoding by learning reusable representations from large-scale heterogeneous neural recordings.

By Linhua Cong, Dingkun Liu, Dongrui Wu

fine-tuning safety

Related stories