arXiv Machine Learning

AMICA-Python: Adaptive Mixture Independent Component Analysis with Anderson Acceleration

arXiv:2607. 18568v1 Announce Type: new Abstract: Adaptive Mixture Independent Component Analysis (AMICA) is widely used in EEG research and has long been associated with strong empirical performance for blind source separation.

arXiv AI

Aug 6

BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding

arXiv:2608. 04156v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instructions, signal processing, quantitative evidence, and scientific interpretation.

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

llms agents 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

Jun 26

Boundary-Aware Context Grounding for A Low-Channel EEG Agent

arXiv:2606. 26519v1 Announce Type: new Abstract: Large language models (LLMs) can make scientific software easier to use.

By Zhiyuan Xu, Yueqing Dai, Junling Li, Junwen Luo

llms agents benchmarks safety

arXiv AI

Jun 29

What the LLM Should Not Say: Boundary-Aware Context Grounding for A Seven-Channel EEG Agent

arXiv:2606. 26519v2 Announce Type: replace Abstract: Large language models (LLMs) can make scientific software easier to use.

By Zhiyuan Xu, Yueqing Dai, Junling Li, Junwen Luo

llms agents benchmarks safety

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 Machine Learning

Aug 4

Not All EEG Moments Are Equal: Position-Adaptive Time Scheduling for EEG Generation

arXiv:2608. 00048v1 Announce Type: cross Abstract: Electroencephalography (EEG) generation is essential for alleviating data scarcity and enabling large scale neural modeling in brain computer interface applications.

By Boheng Liu, Ziyu Li, Chenghua Duan, Qing Li, Xia Wu

diffusion safety

arXiv Machine Learning

Jun 16

libhmm: A Modern C++20 Library for Hidden Markov Models with Correct MLE Emission M-Steps

arXiv:2605. 29208v2 Announce Type: replace-cross Abstract: We describe libhmm, a C++20 library for Hidden Markov Model parameter estimation, sequence decoding, and model selection.

By Gary Wolfman

rag benchmarks

arXiv Machine Learning

2d ago

BCIJelly: An integrated ecosystem for brain-computer interface research

arXiv:2608. 13576v1 Announce Type: cross Abstract: Brain-computer interface (BCI) research relies on multistage computational pipelines, yet progress remains constrained by fragmented data formats, heterogeneous decoder implementations and hardware-specific deployment toolchains, and researchers lack an integrated workflow.

By Liyuan Han, Xinrui Yang, Tianyu Zheng, Qizhi Yang, Yitao Qin, Liang Chen, Qinglai Wei, Binjie Hong, Xinhe Zhang, Rui Xiong, Yong Gu, Mu-ming Poo, Bo Xu, Chengyu Li, Tielin Zhang

llms benchmarks

arXiv Machine Learning

Jun 3

ERP-XTTN: Interpretable Prototype-Guided Cross-Attention for Cross-Subject ERP Classification

arXiv:2606. 02939v1 Announce Type: new Abstract: Interpretable brain-computer interface classifiers that generalize across subjects without calibration remain an open challenge.

By Charlotte Genevier Wyman, Leanne Hirshfield

benchmarks safety

arXiv AI

Jun 26

NeuraDock Visual Cognitive Load Agent Tutorial: A Quality-Gated Open-Source EEG Workflow for Alpha Dynamics and Real-Time Applications

arXiv:2606. 26518v1 Announce Type: new Abstract: This tutorial paper provides a step-by-step, reproducible walkthrough of NeuraDock Agent, an open-source EEG agent focused on Alpha dynamics and visual cognitive-load analysis.

By Zhiyuan Xu, Yueqing Dai, Junling Li, Junwen Luo

llms agents

arXiv Machine Learning

Jul 27

Computer Vision Based Neurology Brain Activity Rejection Architecture and Implementation

arXiv:2607. 21654v1 Announce Type: cross Abstract: The electroencephalogram (EEG) is a valuable and widely applied tool for investigating brain disorders and behavioral changes.

By Zag ElSayed, Nathan Suer, Grace Westerkamp, Jack Yanchen Liu, Makoto Miyakoshi, Craig Erickson, Ernest Pedapati

computer-vision

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

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