When Brain Foundation Model Meets Cauchy-Schwarz Divergence: A New Framework for Cross-Subject Motor Imagery Decoding
当脑基础模型遇见柯西-施瓦茨散度：一种跨受体运动想象解码的新框架
机构 * State Key Laboratory of Mechanical Transmission, College of Mechanical and Vehicle Engineering, Chongqing University（机械传动国家重点实验室，重庆大学）
专题命中 BCI数据与评测 ：motor imagery(title,abstract)；BCI(abstract)；brain-computer interface(abstract)；EEG(abstract)
AI总结 本文提出一种新的多源域适应框架，利用预训练的脑基础模型进行动态源受体选择，并结合柯西-施瓦茨散度实现特征和决策层面对齐，以提高跨受体运动想象解码的准确率。
Comments This work has been submitted to Elsevier for possible publication
