← Back to all news
arXiv Computer Vision By Harshit Goyal

Brain-to-Image Generation: Reconstructing Visual Stimuli from EEG using Generative Adversarial Networks

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Oct 12

Replaying the Movie in Your Mind Decoding Dynamic Visual Perception and Recall from EEG

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
benchmarks
More like this →
arXiv AI
Oct 27

ProtoEEG: Structured Prototype Alignment for EEG Visual Decoding

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
ragdiffusionmultimodalsafety
More like this →
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
multimodalsafety
More like this →