JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
Dr eamDif- fusion: Generating high-quality images from brain EEG sign als
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Janus decouples visual encoding into task-specific pathways inside a single autoregressive transformer to unify multimodal understanding and generation while outperforming earlier unified models.
BraVeSpy reconstructs brain EEG signals from VR headset motion sensors to infer unobservable perceptions and sensitive activities, achieving 52-67% accuracy on images and over 85% on activity fingerprinting.
Generative Visual Grounding creates instance-specific visual proxy images from EEG signals to enhance MLLM understanding of brain activity beyond text-only alignment.
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.
Joint temporal-spectral-spatial EEG encoding with contrastive CLIP alignment sets new SOTA on THINGS-EEG zero-shot decoding, including the first systematic cross-session results.
The paper introduces a time-resolved neural encoder combining Whisper embeddings with recurrent temporal modeling and soft attention to predict ECoG responses, finding strongest alignment in intermediate layers and anatomically coherent phoneme organization in electrodes.
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.
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.
A survey that taxonomizes synthetic brain signal generation methods into four categories, benchmarks them on motor imagery, seizure detection, SSVEP, and auditory attention tasks, and outlines evaluation principles and future directions for data-efficient BCIs.
citing papers explorer
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Let EEG Models Learn EEG
JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
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Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation
Janus decouples visual encoding into task-specific pathways inside a single autoregressive transformer to unify multimodal understanding and generation while outperforming earlier unified models.
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When VR Meets BCI: (Un)Observable Brainwave-aware Privacy Reconstruction in the Metaverse via Unrestricted Inbuilt Motion Sensors
BraVeSpy reconstructs brain EEG signals from VR headset motion sensors to infer unobservable perceptions and sensitive activities, achieving 52-67% accuracy on images and over 85% on activity fingerprinting.
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Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
Generative Visual Grounding creates instance-specific visual proxy images from EEG signals to enhance MLLM understanding of brain activity beyond text-only alignment.
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Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction
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.
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What Does the Brain See? Multiview Neural Representations to Demystify the Brain-Visual Alignment
Joint temporal-spectral-spatial EEG encoding with contrastive CLIP alignment sets new SOTA on THINGS-EEG zero-shot decoding, including the first systematic cross-session results.
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Mapping Whisper Representations to Human ECoG Responses with Interpretable Time-Resolved Neural Encoding
The paper introduces a time-resolved neural encoder combining Whisper embeddings with recurrent temporal modeling and soft attention to predict ECoG responses, finding strongest alignment in intermediate layers and anatomically coherent phoneme organization in electrodes.
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EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience
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.
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BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding
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.
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Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
A survey that taxonomizes synthetic brain signal generation methods into four categories, benchmarks them on motor imagery, seizure detection, SSVEP, and auditory attention tasks, and outlines evaluation principles and future directions for data-efficient BCIs.