75 citations · 99 across the 22 of their papers we have counts for
22 papers
A Cyclic Adaptation-Generalization Framework with Uncertainty-Guided Self-Paced Learning for Long-Term Brain-Machine Interfaces
Jiyu Wei, Di Hong, Zhanjie Zhang +3
Brain-Machine Interfaces (BMIs), which link the brain to external devices, hold great potential in rehabilitation, human performance augmentation, and human-centered robotics. Howe…
Self-Supervised Consistency Enhanced Disentangled Learning for Neural Decoding Generalization in Brain-Machine Interface
Jiyu Wei, Di Hong, Zhanjie Zhang +3
Brain-Machine Interfaces (BMIs) provide a direct communication pathway between the brain and external devices, enabling humans to control assistive and robotic technologies, with p…
TD-DPO: Difference-Aware Preference Optimization for Mitigating Sycophancy in Clinical Autism Intervention Dialogue
Shuzhong Lai, Junhong Lai, Chenxi Li +5
The sycophancy of large language models can increase the safety risk in intervention dialogue for autistic children. Supervised fine-tuning can somewhat reduce sycophancy, but rely…
Neurocircuitry-Inspired Hierarchical Graph Causal Attention Networks for Explainable Depression Identification
Weidao Chen, Yuxiao Yang, Yueming Wang
Major Depressive Disorder (MDD), affecting millions worldwide, exhibits complex pathophysiology manifested through disrupted brain network dynamics. Although graph neural networks…
Human-like Cognitive Generalization for Large Models via Brain-in-the-loop Supervision
Jiaxuan Chen, Yu Qi, Yueming Wang +1
Recent advancements in deep neural networks (DNNs), particularly large-scale language models, have demonstrated remarkable capabilities in image and natural language understanding.…
Improving Unsupervised Task-driven Models of Ventral Visual Stream via Relative Position Predictivity
Dazhong Rong, Hao Dong, Xing Gao +5
Based on the concept that ventral visual stream (VVS) mainly functions for object recognition, current unsupervised task-driven methods model VVS by contrastive learning, and have…
A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives
Junhong Lai, Jiyu Wei, Lin Yao +1
Electroencephalogram (EEG) signals play a crucial role in understanding brain activity and diagnosing neurological diseases. Because supervised EEG encoders are unable to learn rob…
Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation
Di Hong, Yueming Wang
Spiking Neural Networks (SNNs) are promising for low-power computation due to their event-driven mechanism but often suffer from lower accuracy compared to Artificial Neural Networ…
Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs
Yi Jiang, Qingyang Shen, Shuzhong Lai +5
Autism spectrum disorder(ASD) is a pervasive developmental disorder that significantly impacts the daily functioning and social participation of individuals. Despite the abundance…
ASD-Chat: An Innovative Dialogue Intervention System for Children with Autism based on LLM and VB-MAPP
Chengyun Deng, Shuzhong Lai, Chi Zhou +5
Early diagnosis and professional intervention can help children with autism spectrum disorder (ASD) return to normal life. However, the scarcity and imbalance of professional medic…
Speed-enhanced Subdomain Adaptation Regression for Long-term Stable Neural Decoding in Brain-computer Interfaces
Jiyu Wei, Dazhong Rong, Xinyun Zhu +2
Brain-computer interfaces (BCIs) offer a means to convert neural signals into control signals, providing a potential restoration of movement for people with paralysis. Despite thei…
MindGPT: Interpreting What You See with Non-invasive Brain Recordings
Jiaxuan Chen, Yu Qi, Yueming Wang +1
Decoding of seen visual contents with non-invasive brain recordings has important scientific and practical values. Efforts have been made to recover the seen images from brain sign…
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training
Hanwen Wang, Yu Qi, Lin Yao +3
Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. En…
ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks
Jiangrong Shen, Qi Xu, Jian K. Liu +3
Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low powe…
Decoding Chinese phonemes from intracortical brain signals with hyperbolic-space neural representations
Xianhan Tan, Junming Zhu, Jianmin Zhang +2
Speech brain-computer interfaces (BCIs), which translate brain signals into spoken words or sentences, have shown significant potential for high-performance BCI communication. Phon…
LaSNN: Layer-wise ANN-to-SNN Distillation for Effective and Efficient Training in Deep Spiking Neural Networks
Di Hong, Jiangrong Shen, Yu Qi +1
Spiking Neural Networks (SNNs) are biologically realistic and practically promising in low-power computation because of their event-driven mechanism. Usually, the training of SNNs…
Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface
Yu Qi, Xinyun Zhu, Kedi Xu +6
Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for mobil…
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces
Yu Qi, Bin Liu, Yueming Wang +1
Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities. Neural signals recorded from cortex exhibit nonstationar…
Spiking Deep Residual Network
Yangfan Hu, Huajin Tang, Gang Pan
Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional a…
Robust Face Recognition by Constrained Part-based Alignment
Yuting Zhang, Kui Jia, Yueming Wang +3
Developing a reliable and practical face recognition system is a long-standing goal in computer vision research. Existing literature suggests that pixel-wise face alignment is the…
Sparse Principal Component Analysis via Rotation and Truncation
Zhenfang Hu, Gang Pan, Yueming Wang +1
Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, meanwhile the sparse basis should cover…
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut
Zhenfang Hu, Gang Pan, Yueming Wang +1
Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. I…