The paper introduces a multimodal contrastive learning framework that aligns EEG recordings of imagined speech with language representations from a frozen text encoder. By training an EEG encoder with a composite objective that balances semantic reweighting and class discrimination, the method preserves separability among 39 intent categories while embedding EEG signals into a language embedding space. Experiments on the Chisco dataset show comparable decoding performance to a task‑optimized classifier, and additional metrics such as MaxCosine and semantic axes demonstrate intrinsic reliability and broader semantic structure beyond fixed categories.
By Shahryar G. Namdari, Francesco Iacomi, Riccardo Barbieri, Luca Mainardi
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
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
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)
EEG-Captioner introduces a new EEG-to-language framework that grounds language generation in measurable EEG signal properties. The authors built a large dataset of 160,781 EEG segments annotated with question‑answer pairs targeting spectral, spatial, temporal, and lateral properties, and paired each segment with a feature‑grounded caption. Their Dual‑Domain EEG Encoder jointly models temporal waveforms and spectral‑spatial representations, achieving a macro‑averaged balanced accuracy of 88.81% on signal‑level QA and improving caption factual consistency from 82.06% to 83.46% compared to caption‑only models.
By Leyuan Jin, Jiahao Mei, Yixuan Yuan, Shimeng Li, Mengyue Wu, Yue Ding
ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding introduces a model‑agnostic framework that adaptively aligns EEG signals with visual semantics. It replaces fixed visual or textual anchors with EEG‑aware class‑level contrastive supervision and employs structure‑consistent interpolation to preserve channel‑wise and temporal importance. Across multiple evaluation settings—including subject‑dependent, subject‑independent, strict cross‑subject transfer, and continual adaptation—ProCA delivers significant performance gains, achieving relative Top‑1 improvements ranging from 7.4% to 28.1%.
By Kanglei Zhou, Chunyan Lan, Dongyang Li, Jun Zhu, Liyuan Wang
arXiv:2608. 20186v1 Announce Type: new Abstract: Non-invasive decoding of inner speech faces a fundamental data problem: a corpus pairing brain activity with a person's spontaneous inner monologue cannot be collected, and the available proxy paradigms (cued repetitive and retrospectively reported generative inner speech) are slow to acquire, poorly time-locked, and subject compliance is unverifiable.
By Ingo Marquardt, Anthilia Alchanat, Priyanka Jain
ProtoEEG introduces a prototype‑mediated framework for aligning EEG signals with visual data, using a shared 64‑prototype codebook to generate structured representations for retrieval and image generation. The method employs prototype contrast, distribution alignment, instance anchors, and diversity regularization to train the system, achieving state‑of‑the‑art performance on THINGS‑EEG2 and EEG‑ImageNet benchmarks. Ablation studies confirm that prototype contrast and identity preservation are critical for high accuracy, while prototype analyses reveal distributed assignments across the codebook.
By Kieren Yu, Chang Huang, Ziyang Liu, Kaishun Wu
NeuroBridge is a self‑supervised framework for EEG‑to‑image decoding that uses Cognitive Prior Augmentation (CPA) to introduce perceptual variability and a Shared Semantic Projector (SSP) to align EEG and image features in a shared semantic space. The method achieves significant gains over prior work, improving top‑1 accuracy by 12.3% and top‑5 accuracy by 10.2% on a 200‑way zero‑shot retrieval task in intra‑subject experiments. Extensive tests show the approach is robust, scalable, and effective for neural visual decoding.
By Wenjiang Zhang, Sifeng Wang, Yuwei Su, Xinyu Li, Chen Zhang, Suyu Zhong
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
arXiv:2603.13272v2 Announce Type: replace-cross
Abstract: Electroencephalography (EEG) foundation models have shown promise for learning generalizable representations, yet they remain sensitive to ch...
By Hanseul Choi, Jinyeong Park, Seongwon Jin, Sungho Park, Jibum Kim
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