Papers Brain Decoding
“Brain Decoding” 태그가 달린 논문 146편 · 필터 해제
Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness
Brain decoding aims to uncover neural mechanisms by inferring stimulus-related representations from brain signals. In fMRI studies, this is typically achieved by mapping fMRI responses to the latent representations of co…
Adversarial RobustnessFeature ImportanceImage RetrievalBrain DecodingMindAU: EEG-Conditioned Facial Action Unit Editing via Dual-Stream Manifold Alignment
Recent brain decoding studies have made substantial progress in reconstructing externally perceived visual content from neural signals. However, using electroencephalography (EEG) recordings to guide facial expression ed…
Brain DecodingImage EditingDual-Stream EEG Decoding for 3D Visual Perception
This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspired approach implements separate decoding modules for object identity…
3D ReconstructionBrain DecodingEeg DecodingBoosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation
Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate whether and when brain decoding can be boosted by augmenting small …
Data AugmentationBrain DecodingEvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks
Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability. Wh…
Continual LearningBrain DecodingEeg DecodingVersatile Framework with Semantic and Structural guidance for Image Reconstruction from Brain Activity
Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding. Especially, the achievement of precise and controllable image reconstruction bears great significance in p…
Text-to-Image GenerationImage ReconstructionBrain DecodingNeurIPS: Neuro-anatomical Inductive Priors for Sphere-based Brain Decoding
Current fMRI decoders face a performance-fidelity trade-off where efficient ID encoders outperform geometrically faithful surface-based models. We argue this is partly driven by inefficient surface tokenization and the f…
Brain DecodingFPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding
Visual image reconstruction from functional Magnetic Resonance Imaging (fMRI) is a fundamental task in brain decoding, providing a crucial pathway for understanding human perceptual mechanisms and developing advanced bra…
Representation LearningImage ReconstructionBrain DecodingSeeing the imagined: a latent functional alignment in visual imagery decoding from fMRI data
Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diffusion-based generative models. While current pipelines are primarily o…
Brain DecodingBrain-Grasp: Graph-based Saliency Priors for Improved fMRI-based Visual Brain Decoding
Recent progress in brain-guided image generation has improved the quality of fMRI-based reconstructions; however, fundamental challenges remain in preserving object-level structure and semantic fidelity. Many existing ap…
Image GenerationBrain DecodingMeta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field-wide goal is…
Brain DecodingOne Brain, Omni Modalities: Towards Unified Non-Invasive Brain Decoding with Large Language Models
Deciphering brain function through non-invasive recordings requires synthesizing complementary high-frequency electromagnetic (EEG/MEG) and low-frequency metabolic (fMRI) signals. However, despite their shared neural ori…
Brain DecodingASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
Cross-subject generalization in EEG-based brain-computer interfaces (BCIs) remains challenging due to individual variability in neural signals. We investigate whether spectral representations offer more stable features f…
Brain DecodingRAICL: Retrieval-Augmented In-Context Learning for Vision-Language-Model Based EEG Seizure Detection
Electroencephalogram (EEG) decoding is a critical component of medical diagnostics, rehabilitation engineering, and brain-computer interfaces. However, contemporary decoding methodologies remain heavily dependent on task…
Seizure DetectionBrain DecodingUnified Multimodal Brain Decoding via Cross-Subject Soft-ROI Fusion
Multimodal brain decoding aims to reconstruct semantic information that is consistent with visual stimuli from brain activity signals such as fMRI, and then generate readable natural language descriptions. However, multi…
Brain DecodingfMRI2GES: Co-speech Gesture Reconstruction from fMRI Signal with Dual Brain Decoding Alignment
Understanding how the brain responds to external stimuli and decoding this process has been a significant challenge in neuroscience. While previous studies typically concentrated on brain-to-image and brain-to-language r…
Brain DecodingNOIR 2.0: Neural Signal Operated Intelligent Robots for Everyday Activities
Neural Signal Operated Intelligent Robots (NOIR) system is a versatile brain-robot interface that allows humans to control robots for daily tasks using their brain signals. This interface utilizes electroencephalography …
Brain DecodingMultiDiffNet: A Multi-Objective Diffusion Framework for Generalizable Brain Decoding
Neural decoding from electroencephalography (EEG) remains fundamentally limited by poor generalization to unseen subjects, driven by high inter-subject variability and the lack of large-scale datasets to model it effecti…
Data AugmentationBrain DecodingEeg DecodingMindCross: Fast New Subject Adaptation with Limited Data for Cross-subject Video Reconstruction from Brain Signals
Reconstructing video from brain signals is an important brain decoding task. Existing brain decoding frameworks are primarily built on a subject-dependent paradigm, which requires large amounts of brain data for each sub…
Video ReconstructionBrain DecodingA Cognitive Process-Inspired Architecture for Subject-Agnostic Brain Visual Decoding
Subject-agnostic brain decoding, which aims to reconstruct continuous visual experiences from fMRI without subject-specific training, holds great potential for clinical applications. However, this direction remains under…
Contrastive LearningBrain Decoding