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 introduces PAIR, an EEG benchmark that records brain activity from 20 participants while they watch naturalistic video clips and immediately recall them. The dataset allows direct comparison between perception and recall of the same content, and the authors evaluate several decoding models on tasks ranging from fine‑grained semantic classification to meta‑feature decoding (motion, color, object number, face, human presence) and a state‑decoding task distinguishing perception from recall. Results show comparable performance across many model–task pairs, with slightly higher perception accuracy in some coarse‑grained comparisons, and strong separability between perception and recall at the state level, even after removing alpha‑band activity.
By Zikai Fang, Tian-Yi Zhou, Xuan-Hao Liu, Yixi Li, Xiaoyu Xing, Yutong Pei, Bao-Liang Lu, Wei-Long Zheng
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 NEAR, a neural‑anchor‑based retrieval framework that improves brain‑to‑image retrieval when only a few neural trials are available. By treating the high‑repetition center as an anchor, a denoiser pulls noisy queries toward it while a small network predicts pseudo anchors for candidate images, aligning both neural and visual representations. Across EEG, MEG, and fMRI datasets, NEAR consistently boosts retrieval accuracy, notably raising 200‑way Top‑1 accuracy on THINGS‑EEG2 by 5.7–9.3 percentage points with just one to four repetitions.
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:2602. 21819v3 Announce Type: replace-cross Abstract: Reconstructing dynamic visual experiences from brain activity provides a compelling avenue for exploring the neural mechanisms of human visual perception.
By Minghan Yang, Lan Yang, Ke Li, Honggang Zhang, Kaiyue Pang, Yizhe Song
SPARE is a framework for streaming video understanding that adaptively retrieves historical evidence when recent observations are insufficient to answer a question. It uses a lightweight gate to decide when to fetch relevant past clips via a multimodal embedding model, and trains the system with LoRA instruction tuning to jointly use recent and retrieved context while abstaining when evidence is lacking. By sharing a visual encoder between understanding and retrieval branches and aligning features with linear adapters, SPARE achieves state‑of‑the‑art accuracy on OVO‑Bench and StreamingBench, outperforming Qwen3‑VL‑8B‑Instruct by over 20 percentage points on OVO‑Bench and 14 on StreamingBench.
By Senbin Xu, Zijun Zhang, Xinhua Wu, Li He, Jubo Feng, Li Qiao, Zhen Gao, Yan Zhang, Jixiang Luo, Rongrong Ji
FreshMem is a brain-inspired Frequency‑Space Hybrid Memory network designed to improve multimodal large language models’ ability to process streaming video. It combines a Multi‑scale Frequency Memory that compresses excess frames into frequency coefficients while preserving residual details, and a Space Thumbnail Memory that clusters the continuous stream into high‑density episodic thumbnails. Experiments show FreshMem boosts the Qwen2‑VL baseline by 5.20%, 4.52%, and 2.34% on StreamingBench, OV‑Bench, and OVO‑Bench, outperforming several fully fine‑tuned methods without requiring additional training.
By Kangcong Li, Peng Ye, Lin Zhang, Chao Wang, Huafeng Qin, Jiayuan Fan, Tao Chen
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
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:2609.36366v1 Announce Type: cross
Abstract: Understanding how the brain parses actions and events from time-varying natural inputs is a central challenge in neuroscience. Recent work has used d...
By Iishaan Inabathini, Margaret M. Henderson
The study investigates whether self‑supervised learning can produce useful representations from fMRI data alone. Using a joint embedding predictive architecture, participant‑specific encoders are trained without access to stimulus images or derived representations. The resulting embeddings perform competitively on brain captioning, image reconstruction, and cross‑participant generalization, and can be aligned unsupervised across participants, enabling a decoding head trained on one subject to work on another with minimal loss of performance.
By Serwan Jassim, Victoria Bosch, Daniel Anthes, Elia Bruni, Tim Kietzmann