BCIwiki — Brain-computer interface news, research and industry database

On 31 March 2026 Hubei's provincial science and technology department approved the formation of a provincial brain-computer interface technology innovation centre, led by Tongji Hospital in Wuhan together with 12 organisations including Huazhong University of Science and Technology and Hubei Optics Valley Laboratory. Chinese reports describe it as the country's first provincial BCI platform positioned as comprehensive and full-chain. Its stated priorities are neural signal decoding, domestic sourcing of core components, and refining both non-invasive and implanted approaches. It is headed by Tang Zhouping, the hospital's party secretary.

Researchers propose DPMS-Net, a deep network that uses dynamic convolution to mine discriminative cues across temporal, spatial and frequency dimensions, combines channel and temporal attention, and adds a spectral-domain analysis component to surface subtle oscillatory features hidden in the EEG spectrum. On the BCI Competition IV 2a and 2b datasets it reached subject-dependent accuracies of 83.93% and 88.38%, and 67.67% on a self-collected stroke-patient dataset. The authors say its efficient decoding and robustness suit neurorehabilitation BCI systems.

The study presents MAPS-CS, an online SSVEP brain-computer interface that selects channels dynamically during the experiment. A multi-dimensional feature framework covering signal energy, stability and inter-channel correlation quantifies anomalies and generates scores that a hierarchical decision step combines into a channel-quality score to identify and remove bad channels — with no training required. Against the channel ensemble (CE) method, MAPS-CS lifted standard FBCCA accuracy by 3.5%, 4.1%, 4.4% and 6.5% at stimulus durations of 2 s, 1.5 s, 1 s and 0.5 s, the best among the CE, binary harmony search and TOP-K local optimization methods compared.

A randomized controlled trial in 120 patients with post-stroke hemiplegia found that pairing brain-computer interface (BCI) rehabilitation training with a gait robot accelerated lower-limb recovery and improved muscle activation patterns more than either intervention on its own. After eight weeks, BCI training, gait-robot training and the combination all improved lower-limb motor function, muscle activity and gait compared with routine training, but the combined group showed the largest increases in integrated electromyography of the tibialis anterior and gastrocnemius, along with a greater gain in step frequency and a greater reduction in step width.

A new entry in the Chinese Clinical Trial Registry covers a study using brain-computer interface technology for functional recovery after stroke. The record, ChiCTR2600120924, was posted on March 23, 2026, and lists its status as not yet recruiting. The registry listing carries only bibliographic details, so the trial design, sample size and sponsor remain undisclosed.

The FDA cleared Zeto's New Wave System on March 13, 2026, finding it substantially equivalent under submission number K260455 through the special 510(k) pathway. The agency classifies it as a Full-Montage Standard Electroencephalograph, a Class II device under regulation 882.1400, with neurology as the assigned review panel. That category covers standard electroencephalograph hardware acquiring a complete montage of scalp channels.

Nine Jiangsu provincial departments in eastern China led by the Department of Industry and Information Technology issued the Action Plan for Innovative Development of the Brain-Computer Interface Industry in Jiangsu in March 2026, implementing national guidance from seven ministries. Its most distinctive provision covers industrial deployment, directing firms in hazardous materials, nuclear power, mining and electricity to pilot BCI systems in safety management and deep-sea and deep-pit operations, sectors no other provincial document names. Headline goals are at least two provincial clusters by 2027 and two to three leading enterprises by 2030. Its quantitative targets sit at the end of 14 tasks and all fall in 2030, including at least 15 programme projects, 15 key technology tasks, about 10 innovation platforms, 30 certified flagship products, 20 registered medical devices and a dataset of at least 20,000 samples.

A Chinese trial will measure the immediate effects of a non-invasive brain-machine interface combined with multimodal training on neurophysiological function in patients with upper-limb hemiparesis after stroke. It was registered with the Chinese Clinical Trial Registry as ChiCTR2600120046 on March 9, 2026 and has not begun recruiting.

Four Chinese ministries jointly issued science-technology insurance guidance, listing BCI alongside AI and quantum technology as key sectors for dedicated insurance products.

Researchers have proposed MSARFNet, a multi-scale attention-based reconstruction fusion network that reached average classification accuracies of 84.64% and 87.96% on the BCI Competition IV 2a and 2b motor imagery datasets, outperforming several existing methods. The network extracts spatio-temporal features through parallel multi-scale convolutional branches and fuses them with an attention mechanism to sharpen transient motor-imagery responses, targeting the non-stationary EEG signals and inter-subject variability that make MI decoding unreliable. The study was published on February 27, 2026, in IEEE Journal of Biomedical and Health Informatics.