4 citations · 10 across the 11 of their papers we have counts for
11 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…
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…
NeuralOOD: Improving Out-of-Distribution Generalization Performance with Brain-machine Fusion Learning Framework
Shuangchen Zhao, Changde Du, Hui Li +1
Deep Neural Networks (DNNs) have demonstrated exceptional recognition capabilities in traditional computer vision (CV) tasks. However, existing CV models often suffer a significant…
Identifying the Hierarchical Emotional Areas in the Human Brain Through Information Fusion
Zhongyu Huang, Changde Du, Chaozhuo Li +2
The brain basis of emotion has consistently received widespread attention, attracting a large number of studies to explore this cutting-edge topic. However, the methods employed in…
Multi-label Class Incremental Emotion Decoding with Augmented Emotional Semantics Learning
Kaicheng Fu, Changde Du, Xiaoyu Chen +2
Emotion decoding plays an important role in affective human-computer interaction. However, previous studies ignored the dynamic real-world scenario, where human experience a blend…
Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRI
Che Liu, Changde Du, Xiaoyu Chen +1
Drawing inspiration from the hierarchical processing of the human auditory system, which transforms sound from low-level acoustic features to high-level semantic understanding, we…
Open-vocabulary 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…
CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding
Qiongyi Zhou, Changde Du, Shengpei Wang +1
The study of decoding visual neural information faces challenges in generalizing single-subject decoding models to multiple subjects, due to individual differences. Moreover, the l…