arXiv Machine Learning
Oct 16

Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech

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 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)
arXiv Machine Learning
Oct 23

EEG-Captioner: Grounding EEG-to-Language Generation in Fundamental Signal Properties

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
arXiv AI
Sep 7

ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding

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