Multi-Feature Riemannian Hypergraph for Online Test-Time Adaptation of Motor Imagery Brain-Computer Interface
2026-08-17 • Machine Learning
Machine LearningHuman-Computer Interaction
AI summaryⓘ
The authors address two tough problems in brain-computer interfaces that use imagined movements: how to keep the system working well across different days and how to make it adapt during use. They create a method called MRieHy that uses advanced math (Riemannian geometry) to better compare brain signal features over multiple days. MRieHy builds special graphs called hypergraphs to capture complex relationships between brain signals, combining them in a smart way to improve learning. Tests on brain signal datasets show that MRieHy performs better than existing methods in real-time settings.
Motor ImageryBrain-Computer InterfaceRiemannian GeometryHypergraphCross-day TransferabilityOnline AdaptationCovariance MatrixECoGEEG
Authors
Siqi Li, Zhi Li, Tong Liu, Shuai Zhang, Yanfei Jia, Zhiqiang Yi, Jue Xie, Ni Ji
Abstract
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.