Chinese Academy of Medical Sciences & Peking Union Medical College

A team at the Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, has built a hybrid brain-computer interface (BCI) that combines surface electromyography (sEMG) with steady-state visual evoked potentials (SSVEP), encoding 480 targets with 120 flicker frequencies and four hand gestures. In online experiments it achieved a mean classification accuracy of 84.55 ± 7.23% and a mean information transfer rate (ITR), a key measure of practical BCI performance, of 260.07 ± 30.41 bits/min. Its command set matches the largest target counts in existing systems, and the authors place its performance among the top, offering a technical reference for large-command-set BCIs. The work appeared in Cognitive Neurodynamics on September 7, 2026.

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.

Beijing Normal University researchers developed a low-cost structured-illumination miniscope weighing less than 3 g. The system uses a Ronchi grating and time-multiplexed excitation for HiLo imaging, providing optical sectioning in freely behaving mice. It suppresses out-of-focus background fluorescence while retaining the speed, field of view, and accessibility of widefield miniscopes, and it supports optically sectioned multiplane imaging to increase neuronal yield. In hippocampal recordings, the researchers observed better region-of-interest signal quality and spatial-information readout. They also demonstrated a proof-of-principle closed-loop brain-machine interface supported by rapid online signal extraction and real-time neural decoding. The work is a bioRxiv preprint and has not been peer reviewed.

Researchers assessed the feasibility of a hybrid brain-computer interface that integrates motor imagery (MI) and steady-state visual evoked potentials (SSVEP) with robotic glove feedback for upper limb motor rehabilitation in 32 stroke patients, split into a conventional-treatment control group and an experimental group receiving 10- or 20-day BCI interventions. The experimental group showed considerable improvement in Fugl-Meyer scores over the control group, and the BCI achieved EEG classification accuracy up to 98.08% with stable operation; after longer training, accuracy rose, the laterality coefficient moved toward normal, and task-related brain connectivity strengthened. The authors say the hybrid system may overcome the limits of conventional therapy and single-modality BCIs.