Peking University

A collaboration involving Peking University converted dynamic natural movie frames into spatiotemporal patterns of optogenetic stimulation in awake mice, then reconstructed movie frames from responses in the primary visual cortex. Comparing natural viewing with artificial stimulation, they found that stimulation evoked broadly comparable cortical dynamics and aligned representations. A deep learning model reconstructed frames from cortical signals under both conditions. The animal study offers a proof of concept for evaluating how visual cortical prostheses might translate images into stimulation patterns. It does not establish that stimulation produced the corresponding visual experience in mice or restored vision in humans.

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.