发表机构
Peking University; Chinese Institute for Brain Research; NeuCyber Neurotech; Beijing Medical University; Chinese Academy of Medical Sciences & Peking Union Medical College（北京大学; 中国脑科学研究院; 纽赛博神经科技公司; 北京医科大学; 中国医学科学院北京协和医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对MI-BCI解码的跨天可迁移性与在线运行挑战，提出MRieHy框架，通过融合黎曼几何的双超图实现在线测试时自适应，在三类数据集上性能优于现有基线。
AI 中文摘要
在临床运动想象脑机接口（MI-BCI）解码中，跨天可迁移性与在线运行是两大关键挑战。超图可通过捕获高阶样本关系提升迁移性，但现有基于超图的在线情感识别方法忽略了EEG迁移学习中广泛采用的黎曼几何带来的跨天优势。为填补这一空白，我们提出多特征黎曼超图（MRieHy），这一专为MI-BCI解码的在线测试时自适应设计的框架，利用黎曼几何增强跨天可迁移性。MRieHy首先计算跨天训练数据协方差矩阵的黎曼均值以对齐多天分布；接着，基于黎曼距离在协方差矩阵上构建一个超图，辅以基于余弦相似度构建的深度特征上的第二个超图；两个超图通过自适应学习的组合权重融合，与标签投影矩阵联合优化。在线测试期间，MRieHy维护一个最近样本的先进先出缓冲区，对缓冲数据执行黎曼对齐，并利用已学习的超图进行解码。在私有四分类ECoG数据集及两个公开四分类EEG数据集上开展的大量实验验证，MRieHy相比现有最先进基线取得了显著的性能提升。
英文摘要
In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges. Hypergraphs can improve transferability by capturing higher-order sample relationships, yet existing hypergraph-based methods for online emotion recognition neglect the cross-day benefits of Riemannian geometry widely adopted in EEG transfer learning. To bridge this gap, we propose the Multi-feature Riemannian Hypergraph (MRieHy), a framework tailored for online test-time adaptation in MI-BCI decoding that leverages Riemannian geometry to strengthen cross-day transferability. MRieHy first computes Riemannian means of covariance matrices from cross-day training data to align multi-day distributions. It then constructs a hypergraph over covariance matrices using Riemannian distance, complemented by a second hypergraph over deep features built with cosine similarity. The two hypergraphs are fused via adaptively learned combination weights, jointly optimized with the label projection matrices. During online testing, MRieHy maintains a first-in-first-out buffer of recent samples, performs Riemannian alignment on the buffered data, and decodes with the learned hypergraph. Extensive experiments on a private four-class ECoG dataset and two public four-class EEG datasets validate that MRieHy achieves notable performance gains over state-of-the-art baselines.
