{"total":13,"items":[{"citing_arxiv_id":"2606.22182","ref_index":84,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Dual-Stream EEG Decoding for 3D Visual Perception","primary_cat":"cs.CV","submitted_at":"2026-06-20T18:25:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.29591","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Mind-Omni: A Unified Multi-Task Framework for Brain-Vision-Language Modeling via Discrete Diffusion","primary_cat":"cs.AI","submitted_at":"2026-05-28T08:33:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Mind-Omni unifies seven brain-vision-language tasks in one discrete-diffusion framework with a brain tokenizer and a new BQA dataset, claiming SOTA multi-task performance competitive with larger single-task models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.24523","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MindAlign: Bridging EEG, Vision, and Language for Zero-Shot Visual Decoding","primary_cat":"cs.LG","submitted_at":"2026-05-23T11:23:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A tri-modal contrastive learning method for EEG-based zero-shot visual decoding reports 54.1% top-1 accuracy on the Things-EEG2 200-way benchmark, outperforming prior baselines of 32.4%.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23996","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Brain-to-Image Retrieval and Reconstruction via Multimodal EEG Alignment","primary_cat":"cs.CV","submitted_at":"2026-05-18T05:33:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A multimodal alignment pipeline decodes EEG signals recorded during natural image viewing into image retrieval (86.3% Top-1) and reconstruction (CLIP 0.903) tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14569","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction","primary_cat":"cs.CV","submitted_at":"2026-05-14T08:39:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CineNeuron improves fMRI-to-video reconstruction by combining bottom-up semantic enrichment with top-down Mixture-of-Memories integration and outperforms prior methods on benchmarks.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"events through a video fmri dataset and metadata.bioRxiv, pages 2023-03, 2023. 5, 6 [49] Chong Li, Xuelin Qian, Yun Wang, Jingyang Huo, Xi- angyang Xue, Yanwei Fu, and Jianfeng Feng. Enhanc- ing cross-subject fmri-to-video decoding with global-local functional alignment. InEuropean Conference on Com- puter Vision, pages 353-369. Springer, 2024. 3, 6, 4 [50] Dongyang Li, Chen Wei, Shiying Li, Jiachen Zou, Haoyang Qin, and Quanying Liu. Visual decoding and reconstruction via eeg embeddings with guided diffusion.arXiv preprint arXiv:2403.07721, 2024. 1 [51] Quanhao Li, Zhen Xing, Rui Wang, Hui Zhang, Qi Dai, and Zuxuan Wu. Magicmotion: Controllable video generation with dense-to-sparse trajectory guidance."},{"citing_arxiv_id":"2605.04680","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding","primary_cat":"cs.CV","submitted_at":"2026-05-06T09:31:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MB2L achieves 80.5% top-1 and 97.6% top-5 accuracy on zero-shot EEG-to-image retrieval by using biomimetic modules and bidirectional contrastive learning to align neural and visual features.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26218","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projection","primary_cat":"cs.CV","submitted_at":"2026-04-29T01:53:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"ViBE generates M/EEG signals from visual stimuli by reconstructing neural responses with a TSC-VAE and aligning CLIP image features to its latent space via Q-Former, MSE, and sliced Wasserstein losses.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.09817","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity","primary_cat":"cs.LG","submitted_at":"2026-04-10T18:49:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.08537","ref_index":58,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding","primary_cat":"cs.LG","submitted_at":"2026-04-09T17:59:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A meta-optimized in-context learning approach enables training-free cross-subject semantic visual decoding from fMRI by inferring individual neural encoding patterns via hierarchical inference on a few examples.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Scaling vision transformers for functional mri with flat maps.arXiv preprint arXiv:2510.13768, 2025. 3 [57] Alexander Lappe, Anna Bogn 'ar, Ghazaleh Ghamkahri Nejad, Albert Mukovskiy, Lucas Martini, Martin Giese, and Rufin V ogels. Parallel backpropagation for shared-feature visualization.Advances in Neural Information Processing Systems, 37:22993-23012, 2024. 2 [58] Dongyang Li, Chen Wei, Shiying Li, Jiachen Zou, Haoyang Qin, and Quanying Liu. Visual decoding and reconstruction via eeg embeddings with guided diffusion.arXiv preprint arXiv:2403.07721, 2024. 3 [59] Yipeng Li, Wei Jin, Jia Yang, Wanru Li, Baoqi Gong, Xieyi Liu, Zhengxin Gong, Kesheng Wang, Zishuo Zhao, Jingqiu Luo, et al. Triple-n dataset: Non-human primate neural responses to natural scenes."},{"citing_arxiv_id":"2604.08063","ref_index":22,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience","primary_cat":"cs.CV","submitted_at":"2026-04-09T10:25:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"EEG2Vision reconstructs images from EEG using diffusion models plus LLM-guided boosting, with reconstruction quality holding up reasonably as electrode count drops from 128 to 24 channels.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.03181","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery","primary_cat":"cs.RO","submitted_at":"2026-03-03T17:41:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A hybrid visual-motor imagery EEG decoder controls a robot for grasping and placement at 40% and 63% accuracy respectively, yielding 21% end-to-end task success in cue-free online use.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.18187","ref_index":57,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding","primary_cat":"cs.CV","submitted_at":"2025-08-25T16:44:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"BRAIN uses bias-mitigation continual learning with a new de-bias contrastive loss and angular forgetting mitigation to achieve SOTA performance on vision-brain understanding benchmarks despite brain signal inconsistencies across sessions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":", Liu, Y., Yang, L.: Visual neural decoding via improved visual- eeg semantic consistency. arXiv preprint arXiv:2408.06788 (2024) [56] Du, C., Fu, K., Li, J., He, H.: Decoding visual neural representations by multi- modal learning of brain-visual-linguistic features. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(9), 10760-10777 (2023) [57] Li, D., Wei, C., Li, S., Zou, J., Qin, H., Liu, Q.: Visual decoding and reconstruc- tion via eeg embeddings with guided diffusion. arXiv preprint arXiv:2403.07721 (2024) [58] Song, Y., Liu, B., Li, X., Shi, N., Wang, Y., Gao, X.: Decoding natural images from eeg for object recognition. arXiv preprint arXiv:2308.13234 (2023) [59] Radford, A., Kim, J."},{"citing_arxiv_id":"2502.21154","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation","primary_cat":"cs.HC","submitted_at":"2025-02-28T15:32:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Hyper-MML integrates EEG, audio, and video using an Adaptive Brain Encoder with Mutual-cross Attention (ABEMA) and Adaptive Hypergraph Fusion Module (AHFM) to outperform prior methods on EAV and AFFEC datasets for conversational emotion recognition.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}