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Shenzhen Science and Technology Innovation Bureau forwards Guangdong's notice soliciting BCI technology industry application needs and major achievements.

An arXiv preprint adapts the computationally intensive MindEye2 pipeline for real-time reconstruction of perceived natural images from fMRI. Using the open-source RT-Cloud platform, the researchers decoded single-trial visual perception within seconds of image presentation and analyzed the factors behind performance changes from offline to real-time processing. The work has not been peer reviewed.

A preprint proposes interpretable metrics that separately quantify temporal, spatial, and frequency variability in BCI-related brain activity, tested across two motor-imagery BCI datasets totaling 133 participants and validated through within-subject and cross-subject classification experiments. The researchers report negative correlations of -0.2 to -0.4 under most conditions, indicating lower variability tracks higher BCI performance, and note the metrics show deep-learning and Riemannian classifiers differ in robustness to variability, with weaker correlations for the former. The work has not been peer reviewed.

The study shows mouse primary motor cortex encodes locomotion speed through a sigmoidal state-transition mechanism carried by two functionally distinct spiking populations, a framework that also extends to local field potential (LFP) band power. Using chronic 32-channel laminar arrays in 8 mice, the team recorded 5,889 single units across 384 channels and clustered them into speed-positively related (70.8%) and speed-inversely related (29.2%) groups sharing a speed threshold of about 2.3 m/min. The minority speed-inversely related population decoded speed more accurately via inverse-sigmoid transformation, generalizing across animals. The authors say the findings point toward stable, calibration-light brain-machine interface design.

NeuroXess registered a prospective, multicenter, single-arm trial (NCT07720882) enrolling people with tetraplegia caused by spinal cord injury to evaluate the safety and clinical efficacy of an implantable BCI system for compensatory hand movement and upper-limb functional replacement. The study is at the registration stage with no results yet.

Researchers have paired steady-state motion visual evoked potentials (SSMVEP) with electromyography (EMG) in a hybrid brain-computer interface, using a parallel architecture to expand the command space from 15 targets to 60. The multimodal setup reached an information transfer rate of 62.33 bits/min, against 42.49 bits/min for the best single-modality condition. Deep-learning decoding of the two signal streams held classification accuracy steady while lowering the effort required of users with severe motor impairment.

A post from SpikeGadgets surveys how far multichannel electrophysiology has scaled: its Modular Stacking Headstage supports chronic recording of 1,024 channels across 10 brain regions; the University of Pittsburgh's MePhys platform uses 992 electrode contacts to cover an entire macaque hemisphere; and a Rice University preprint describes a custom ASIC that supports 5,376 simultaneous recording channels.

Three stroke patients with hemiplegia improved on upper-limb motor scores and daily-living measures after 24 sessions of visually guided brain-computer interface training paired with functional electrical stimulation, according to a case report. The patients trained five times a week, and stimulation fired only when the system detected motor imagery-related EEG patterns; gains appeared on Brunnstrom stages, the Fugl-Meyer Assessment and the Barthel Index, most clearly in wrist and hand control. The report cautions that the findings are preliminary and require validation in larger randomized controlled trials.