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EEG signals are only microvolts strong, so blinks, jaw clenching and muscle activity easily contaminate them. Researchers at Tianjin University and Tiangong University in northern China proposed MTGNet, a framework that suppresses these artifacts while preserving the information downstream tasks need. On the public EEGDenoiseNet dataset, it cut spectral relative root-mean-square error by 18.9%, 31.5% and 14.0% for EMG, EOG and mixed artifacts respectively; on a real-world fatigue EEG dataset, it raised classification accuracy by 6.20 to 6.69 percentage points over unprocessed input. Adapting the framework to a new task takes only 0.33 million low-rank adaptation (LoRA) parameters and no paired clean EEG reference.

LibriBrain100, a magnetoencephalography (MEG) dataset released on August 25, 2026, contains more than 100 hours of high-quality recordings, about 80 of them from a single subject, the deepest within-subject collection of its kind. It is meant as a standardized benchmark for neural speech decoding and ships with open-source tooling and an online competition; a further 32 subjects contribute roughly 40 minutes each to offset thin per-subject data. Using an existing decoding model, the team reported state-of-the-art results on a word-classification benchmark, which it takes as evidence of both data quality and the value of deep within-subject recording.

A preprint introduces the von Neumann State-Space Transformer (VN-SST), a neural decoding model that is more data-efficient than a modern Transformer across three motor-cortex decoding benchmarks, with the largest gains where training data is scarce.

Researchers at Sapienza University of Rome had 20 healthy young adults complete 10 consecutive blocks of finger tapping, then used transcranial magnetic stimulation (TMS) to track motor cortex excitability over the following 60 minutes. Excitability rose after the fatiguing task and drifted back to baseline, and participants whose tapping slowed most showed the largest increases. The findings were published in Clinical Neurophysiology on August 25, 2026, and the authors say they offer a framework for studying altered compensatory responses in neurological disorders.

Brain-computer interfaces are moving out of neurology and into orthopedic rehabilitation, according to a review arguing that BCIs can raise corticospinal excitability and induce neuroplasticity by decoding movement-related neural signals and closing a feedback loop between central and peripheral systems. The target is postoperative muscle inhibition caused by insufficient central motor drive; available evidence suggests motor imagery-based BCI training improves quadriceps voluntary activation and limits strength loss after ACL reconstruction, though the authors cite thin mechanistic evidence, patient heterogeneity and a lack of standardized protocols as barriers to translation. The review, by researchers at the First Affiliated Hospital of Jinan University in southern China's Guangzhou and other institutions, was published on August 25, 2026, in the Chinese Journal of Reparative and Reconstructive Surgery.

P300 spellers are a common brain-computer interface communication tool for people with ALS, and researchers have begun adding large language models as language priors to raise spelling accuracy. But accuracy alone cannot show whether an output came from the patient's brain signals or the model's prediction. A retrospective study re-decoded 3,373 P300 speller selections from 47 people with ALS, fusing reconstructed neural posteriors with 25 language priors ranging from 5-gram models to a 46.7-billion-parameter model. The prior accounted for a participant-weighted mean of 8.6% of posterior displacement, a neural contribution fraction of 0.914. In 4.4% of selections, the fused system produced the intended character where neural evidence alone would not have. Results were similar across 21 neural language models. The work is a preprint and has not been peer reviewed.

BCIFlex Medical Technology has begun a clinical trial in China evaluating the safety and efficacy of an implantable wireless brain-computer interface in patients left tetraplegic by spinal cord injury. The trial is registered on ClinicalTrials.gov as NCT07784088 and is recruiting. The sponsor is a Chinese device maker headquartered in the Haidian district of Beijing with an office in Shanghai, working on invasive ultra-thin flexible electrodes; its products include digital EEG systems and stereo-EEG depth electrodes.

National University Hospital in Singapore has registered a clinical trial (NCT07784920) evaluating Lifescapes, an EEG-based brain-computer interface, for hand motor recovery after stroke. The 32-participant study, not yet recruiting, will compare Lifescapes therapy delivered with reduced therapist supervision against conventional rehabilitation and assess whether it is operationally feasible in the local clinical setting. Lifescapes combines motor imagery practice, biofeedback, neuromuscular electrical stimulation and robotic assistance for stroke patients with severe hand paralysis; an earlier trial in 40 patients showed improvement in motor function.

A multicenter trial has begun in China to test closed-loop brain-computer interface neuromodulation for treatment-resistant depression, run jointly by the Shanghai Mental Health Center and two Beijing hospitals, Anding Hospital and Xuanwu Hospital. The study will compare closed-loop deep brain stimulation and transcutaneous auricular vagus nerve stimulation against sham stimulation, enrolling patients who have responded inadequately to standard antidepressant treatment. It uses a patient-preference design with randomized assignment within each intervention group.