Meta-AlignNN: A meta-learning framework for stable brain-computer interface performance across subjects, time, and tasks
Yongjie Zou, Kai Liu, Congcong Zhang, Yu Ling, Fei Wang, Mingxin Li, Yulei Chen, Mengyu Li, Shouliang Guan, Zhenliang He, Chengyu Li
This paper introduces Meta-AlignNN, a meta-learning framework that improves BCI stability across subjects, time, and tasks by leveraging neural population dynamics consistency.
bioRxiv · Apr 20, 2025 · PreprintBioengineering