arXiv Machine Learning

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.

arXiv AI

Jun 2

Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding

arXiv:2603. 03312v3 Announce Type: replace-cross Abstract: Decoding natural language from non-invasive EEG signals is a promising yet challenging task.

By Yuchen Wang, Haonan Wang, Yu Guo, Honglong Yang, Xiaomeng Li

llms rag benchmarks 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 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 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 24

Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces

arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.

By Siyang Li, Jiayi Ouyang, Zhenyao Cui, Ziwei Wang, Tianwang Jia, Feng Wan, Dongrui Wu

safety

arXiv AI

5d ago

Cueless EEG imagined speech for subject identification: dataset and benchmarks

arXiv:2501. 09700v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG) signals have emerged as a promising modality for biometric identification.

By Ali Derakhshesh, Zahra Dehghanian, Reza Ebrahimpour, Hamid R. Rabiee

benchmarks

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 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

Jul 29

Joint Text-Audio Alignment for EEG-to-Text Decoding in Chinese Speech Production and Perception

arXiv:2607. 25626v1 Announce Type: new Abstract: Decoding speech information directly from scalp electroencephalography (EEG) into text provides a potential non-invasive neural communication pathway for individuals with severe speech and motor impairments.

By Tian Zheng, Xurong Xie, Xinxin Zhu, Xiaolan Peng, Feng Tian

rag benchmarks safety

arXiv Machine Learning

22h 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

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 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

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