arXiv Machine Learning

Automating Learner Assessment: Benchmarking Machine Learning and Deep Learning Models for EEG-Based Familiarity Prediction

arXiv:2608. 16541v1 Announce Type: cross Abstract: Objective assessment of learning remains a fundamental challenge in education.

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 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

Understanding and Correcting Low-Frequency Bias in EEG Foundation Model

arXiv:2608. 01898v1 Announce Type: new Abstract: Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance.

By Junjie Yu, Zihan Deng, Jianyu Zhang, Junrong Mu, Jiahui An, Wenxiao Ma, Ziling Lu, Yue Wang, Yan Zhu, Kexin Lou, Quanying Liu

benchmarks safety

arXiv AI

Jun 16

EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models

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.

By Wei Xiong, Jiangtong Li, Jie Li, Kun Zhu, Changjun Jiang

fine-tuning 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 AI

Jul 3

Single-Channel EEG-Based Cognitive Load Assessment in Online Learning: A Hybrid Deep Learning Approach

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.

By Rowan Hussein, Mohamed Ouf

arXiv Machine Learning

Jun 25

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.

By Matin Fallahi, Patricia Arias-Cabarcos, Thorsten Strufe

llms rag fine-tuning

arXiv Machine Learning

6d ago

Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models

arXiv:2608. 11656v1 Announce Type: new Abstract: Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments, and datasets.

By Myeong-Ju Cho, Hye-Bin Shin, Seo-Hyun Lee, Seong-Whan Lee

llms rag diffusion 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

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

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

arXiv AI

Jun 2

EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks

arXiv:2606. 01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability.

By Yangxuan Zhou, Sha Zhao, Jiquan Wang, Shijian Li, Gang Pan

fine-tuning efficiency benchmarks

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