5 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…
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…
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…