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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.

Two people with tetraplegia typed on a virtual two-handed QWERTY keyboard simply by attempting finger movements, which electrodes implanted in the brain decoded into keystrokes. Typing speed was set by the users and reached 110 characters per minute, or 22 words per minute, with a word error rate of 1.6%, which the authors say resembles able-bodied typing accuracy. Accurate decoding required as few as 30 calibration sentences, and a 5-gram language model improved sentence output. One participant has amyotrophic lateral sclerosis (ALS) and the other a spinal cord injury.

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.

Researchers built STAND-Net, a spiking neural network that cleans blink, muscle and other artifacts from EEG and computes only when the signal changes, combining a leaky integrate-and-fire encoder-decoder, a dilated-convolution residual backbone and an attention mechanism that locates artifacts from neuron firing patterns. Against state-of-the-art methods it raised signal-to-distortion ratio by more than 3.7 dB across artifact types and used 97.98% less power than comparable deep neural networks; BCI classification on the cleaned signals was 6.64% more accurate. The results come from the paper's abstract, with no data from wearers.

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.

The Chinese Clinical Trial Registry (ChiCTR) has logged a study of a brain-computer interface (BCI)-enabled cognitive rehabilitation system, set to test its clinical effect in patients with mild cognitive impairment. Registered on February 25, 2026 as ChiCTR2600119334, the trial is not yet recruiting.