6 papers
Versatile Framework with Semantic and Structural guidance for Image Reconstruction from Brain Activity
Yizhuo Lu, Changde Du, Qiongyi Zhou +2
Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding. Especially, the achievement of precise and controllable image reco…
Mind-Omni: A Unified Multi-Task Framework for Brain-Vision-Language Modeling via Discrete Diffusion
Yizhuo Lu, Changde Du, Qingyu Shi +5
Modeling the interplay between external stimuli and internal neural representations is a pivotal research area for Brain-Computer Interfaces (BCIs). A major limitation of prior wor…
Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports
Changde Du, Yizhuo Lu, Zhongyu Huang +4
The ability to represent emotion plays a significant role in human cognition and social interaction, yet the high-dimensional geometry of this affective space and its neural underp…
Human-like object concept representations emerge naturally in multimodal large language models
Changde Du, Kaicheng Fu, Bincheng Wen +10
Understanding how humans conceptualize and categorize natural objects offers critical insights into perception and cognition. With the advent of Large Language Models (LLMs), a key…
BP-GPT: Auditory Neural Decoding Using fMRI-prompted LLM
Xiaoyu Chen, Changde Du, Che Liu +2
Decoding language information from brain signals represents a vital research area within brain-computer interfaces, particularly in the context of deciphering the semantic informat…
Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity
Yizhuo Lu, Changde Du, Chong Wang +4
Reconstructing human dynamic vision from brain activity is a challenging task with great scientific significance. Although prior video reconstruction methods have made substantial…