Peking University

Implantable brain-computer interfaces are shifting from rigid silicon architectures to soft, structurally adaptive systems built for seamless, long-term integration with neural tissue, according to a review of flexible electrode materials and structural design published in SmartMat on August 31, 2026. Breakthroughs in materials science and micro/nanofabrication have given this generation of devices mechanical compliance, robust interfacial adhesion and high-fidelity signal acquisition that earlier designs could not reach, the review says. Long-term stability at the electrode-tissue interface remains one of the core bottlenecks for invasive BCI.

Researchers propose MRieHy, a multi-feature Riemannian hypergraph framework for online test-time adaptation of motor imagery BCI decoding. It aligns multi-day distributions via Riemannian means of covariance matrices, builds one hypergraph with Riemannian distance and a second with cosine similarity, fuses them with adaptively learned weights, and decodes buffered online samples after Riemannian alignment. On a private four-class ECoG dataset and two public four-class EEG datasets, MRieHy shows notable gains over state-of-the-art baselines, targeting the cross-day transferability and online operation that clinical MI-BCI still lacks.