SCORE: Subject Coordinate Recovery for Label-Free Cross-Subject EEG-to-Image Retrieval proposes a new framework that aligns EEG signals from different subjects into a common image space without requiring labeled calibration data. By training on source subjects and estimating an orthogonal transformation at deployment, SCORE recovers target EEG coordinates and selects reliable EEG-image landmarks through hubness-corrected matching. The method achieves state‑of‑the‑art Top‑1/Top‑5 accuracy on two public benchmarks, outperforming existing baselines by significant margins.
By Zhenyao Cui, Siyuan Kan, Siyang Li, Ziwei Wang, Dongrui Wu
arXiv:2609.24136v1 Announce Type: new
Abstract: Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented...
By Ye Wang, HaoKun Ren, Hong Yu, Ruirui Li, Xiao Li, Ke Liu, Wei Wu
Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented by pretrained vision models, whose internal repr...
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:2606. 16462v1 Announce Type: cross Abstract: Cross-subject EEG decoding promises more training data, but it also exposes neural networks to strong inter-subject distribution shifts.
By Bruna J. Lopes, Gabriel Schwartz, Sylvain Chevallier, Raphael Y. de Camargo, Bruno Aristimunha
The paper introduces an adaptive cortically constrained method for aligning EEG signals with visual representations in zero‑shot brain‑to‑image retrieval. It reconstructs EEG into ROI‑level source patterns, encodes them with a Neuro‑ROI Attention Encoder, and applies evidence‑based adaptive visual supervision to account for response‑wise variability. On the THINGS‑EEG dataset, the approach achieves strong 200‑way retrieval performance and offers ROI‑level attribution for interpretability.
By Ye Wang, Haokun Ren, Wei Wu, Guoyin Wang, Zhuliang Yu, Hong Yu, Ke Liu
arXiv:2606. 23706v1 Announce Type: cross Abstract: The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective neural biomarkers in mental health.
By Baimam Boukar Jean Jacques, Brandone Fonya, Nchofon Tagha Ghogomu, Pauline Nyaboe, Kipngeno Koech
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.
By Targol Bakhtiarvand, Jugal Kalita, Adham Atyabi
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
The paper presents a reproducible single‑subject baseline for reconstructing visual stimuli from EEG using a temporal‑spatial convolutional encoder that maps averaged EEG signals to 512‑dimensional ViT-B/32 image features. On the THINGS‑EEG2 dataset, the model achieves 12.83%, 39.17%, and 58.00% image recall at ranks 1, 5, and 10, respectively, outperforming analytical chance levels. The study also shows that performance drops sharply when applying a model trained on one subject to others, and that direct conditional generators without external visual weights produce noise‑dominated outputs, indicating that only coarse semantic decoding is feasible under the tested protocol.
By Harshit Goyal
arXiv:2605.23137v3 Announce Type: replace-cross
Abstract: Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured...
By Jiahe Meng, Weiming Zeng, Yueyang Li, Bo Chai, Hongjie Yan, Zhiguo Zhang, Wai Ting Siok, Nizhuan Wang
arXiv:2606. 15989v1 Announce Type: cross Abstract: Aligning neural activity across subjects offers the promise of discovering shared computational principles and generalizable decoders.
By Angeliki Papathanasiou, Jascha Achterberg, Thomas E. Nichols, Rui Ponte Costa