arXiv:2609.36971v1 Announce Type: new
Abstract: Cross-subject EEG-to-image retrieval requires a neural represen- tation trained on source subjects to remain aligned with a visual embedding space for...
By Salini Yadav, Taveena Lotey, Micka\"el Coustaty, Pravendra Singh, Partha Pratim Roy
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
The paper introduces the Residual Patch Adapter (RPA), a lightweight module that aligns EEG and MEG signals with all patch tokens from an intermediate layer of a Vision Transformer, rather than a single global embedding. Ablation studies show that retaining the full set of patch tokens is crucial for effective alignment, while the CLS token adds little value. Using RPA, the authors achieve state‑of‑the‑art Top‑1 accuracies on THINGS‑EEG2 and THINGS‑MEG datasets, and demonstrate that the approach generalizes to other brain encoders and can be swapped with various neural network backbones.
By Yuhui Jin, Yonghao Song, Bingchuan Liu
The paper introduces BFM-MSDA, a brain foundation model-guided multi-source domain adaptation framework for cross-subject motor‑imagery EEG decoding. It first uses pretrained brain foundation model representations to estimate source–target compatibility and select relevant source subjects. A relevance-weighted dual alignment strategy then aligns marginal and conditional feature distributions, achieving higher accuracies on two public MI‑EEG benchmarks and demonstrating the benefits of informed source selection.
By Jinzhou Wu, Baoping Tang, Qikang Li, Yi Wang, Cheng Li, Shujian Yu
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
The paper introduces NEAR, a neural-anchor-based retrieval framework that improves brain-to-image retrieval when only a few neural trials are available. By treating a high‑repetition center as an anchor, NEAR uses a denoiser to pull noisy queries toward the anchor and a small network to predict pseudo anchors for candidate images, thereby aligning both neural and visual representations. Experiments on EEG, MEG, and fMRI datasets show consistent gains, including a 5.7–9.3 percentage point improvement in 200‑way Top‑1 accuracy on THINGS‑EEG2 with only one or four repetitions.
By Zhenyao Cui, Siyuan Kan, Dingkun Liu, Dongrui Wu