arXiv AI

Transition-Related Potentials as Markers of Narrative Comprehension in Continuous EEG

arXiv:2607. 20720v1 Announce Type: cross Abstract: Harnessing the potential of electroencephalography (EEG) for brain research is fundamentally limited by intrinsic noise and the diffuse projection of brain-generated activity over the scalp.

arXiv Machine Learning

Jul 3

NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding

arXiv:2601. 01095v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand temporally unfolding narratives in videos remains underexplored.

By Hyeonjeong Ha, Jinjin Ge, Bo Feng, Kaixin Ma, Gargi Chakraborty

llms multimodal benchmarks

arXiv AI

Jun 2

Versatile Framework with Semantic and Structural guidance for Image Reconstruction from Brain Activity

arXiv:2606. 00121v1 Announce Type: cross Abstract: Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding.

By Yizhuo Lu, Changde Du, Qiongyi Zhou, Liuyun Jiang, Huiguang He

rag diffusion 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

1d ago

SemVideo: Reconstructs What You Watch from Brain Activity via Hierarchical Semantic Guidance

arXiv:2602. 21819v3 Announce Type: replace-cross Abstract: Reconstructing dynamic visual experiences from brain activity provides a compelling avenue for exploring the neural mechanisms of human visual perception.

By Minghan Yang, Lan Yang, Ke Li, Honggang Zhang, Kaiyue Pang, Yizhe Song

rag benchmarks safety

arXiv AI

Jun 6

Brain-CLIPLM: Semantic Compression for EEG-to-Text Decoding

arXiv:2604. 16370v2 Announce Type: replace-cross Abstract: Decoding natural language from non-invasive electroencephalography (EEG) remains constrained by low signal-to-noise ratio and limited information bandwidth.

By Xiaoli Yang, Huiyuan Tian, Yurui Li, Jianyu Zhang, Shijian Li, Gang Pan

llms benchmarks

arXiv Machine Learning

Jun 11

Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings

arXiv:2606. 11415v1 Announce Type: cross Abstract: Neural recordings are often interpreted as local measurements, yet the signal at any one sensor can also reflect structured activity distributed across the broader network.

By Maryam Ostadsharif Memar, Nima Dehghani

arXiv Machine Learning

2d ago

EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models

arXiv:2608. 13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG.

By Deeksha M Shama, Punnisa Amornsirikul, Archana Venkataraman

safety

arXiv Machine Learning

Jul 22

Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark

arXiv:2607. 18749v1 Announce Type: new Abstract: Translating brain signals into text could restore communication for people with severe paralysis, yet practically usable systems to date rely on invasive electrocorticography (ECoG).

By Zihan Zhang (Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology), Yu Bao (Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, Shanghai Innovation Institute), Xiao Ding (Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology), Tianyi Jiang (State Key Laboratory for Novel Software Technology, Nanjing University), Kai Xiong (Zhongguancun Laboratory)

benchmarks

arXiv AI

Jun 2

EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors

arXiv:2606. 01884v1 Announce Type: new Abstract: Practical non-invasive Brain-Computer Interface (BCI) systems require EEG decoders with strong cross-subject generalization and minimal calibration.

By Ziyuan Li, Yueyu Sun, Yimeng Zhang

efficiency multimodal benchmarks

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 Machine Learning

Aug 4

Rethinking PPG-based Sleep Staging: Datasets, Metrics, and Benchmarks

arXiv:2608. 00943v1 Announce Type: cross Abstract: Automated sleep staging assigns discrete stage labels to successive time epochs throughout an overnight recording; conventionally each window spans at least 30 seconds, reflecting the minimum temporal resolution of the clinical scoring standard.

By Shuntian Zheng, Jiawei Wang, Cong Fu, Huan Yu, Chen Chen, Yu Guan, Sai Gu

benchmarks

arXiv Machine Learning

Jul 30

SENSE: Efficient EEG-to-Text via Privacy-Preserving Semantic Retrieval

arXiv:2603. 17109v2 Announce Type: replace Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction.

By Akshaj Murhekar, Christina Liu, Abhijit Mishra, Shounak Roychowdhury, Jacek Gwizdka

llms rag fine-tuning safety

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