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Surgeons in Michigan have implanted Paradromics' Connexus brain-computer interface in a patient with motor neuron disease who had lost most of her ability to speak. Connexus records from individual neurons through 421 microelectrodes seated 1.5 millimeters into the motor cortex; the signals pass to a transceiver in the patient's chest, where AI converts them into text on a screen. Austin, Texas-based Paradromics received FDA approval in the fall of 2025 to begin the clinical trial.

The University of Texas at Austin has registered a study on ClinicalTrials.gov to explore whether an EEG-based brain-computer interface (BCI) decoding the P300 event-related potential in real time, combined with non-invasive interventions such as mindfulness relaxation or transcranial electrical stimulation, can enhance attention and memory neural markers—proxies for cognitive reserve—in healthy older adults and those with mild cognitive impairment (MCI). The trial is recruiting and aims to test whether targeted modulation of attention-related brain activity can support cognitive reserve.

UC Irvine researchers tested an EEG category-decoding task in 30 participants viewing pictures and words from five semantic groups. All picture-category pairs were statistically separable, but only one word-category pair was; parietal and left-temporal electrodes contributed more to picture decoding than frontal and right-temporal sites. The bioRxiv preprint has not been peer reviewed.

Researchers report a preprint, posted to arXiv on July 25, 2026 and not yet peer reviewed, that contrasts Bayesian complete-pooling models against frequentist baselines for cross-subject, left-hand versus right-hand motor imagery EEG classification across 20 datasets. Six frequentist pipelines were each paired with an analogous Bayesian pipeline sharing identical feature engineering and fit via Markov chain Monte Carlo posterior sampling. Bayesian complete-pooling produced statistically but not practically significant improvements in reliability and increased predictive uncertainty, with no significant differences in Brier score, resolution, or discrimination. Bayesian pipelines consumed roughly 13 times more energy than their frequentist counterparts, and the authors conclude that complete pooling alone offers limited practical benefit, pointing to partial pooling as a more promising direction.

A registry-based cross-sectional analysis found only 11 pediatric brain-computer interface (pBCI) clinical trials worldwide, spanning 7 countries. Eight evaluated non-implanted devices and 3 evaluated implanted systems. Non-implanted trials had a median enrollment of 29 participants and median duration of 56.0 days; implanted trials had a median enrollment of 8 and median duration of 365.3 days. Only 4 studies enrolled exclusively pediatric participants; the rest recruited both children and adults. The authors conclude that current pBCI research is limited in scope and that children may be inadequately prioritized.

Placing electrodes on the brain's surface rather than inserting them into tissue is the key difference between Precision Neuroscience's approach and the BrainGate approach Donoghue pioneered. The New York-based company develops minimally invasive, safely removable, high-bandwidth BCIs; its first system, Layer 7, is aimed at people with ALS, spinal cord injury and brainstem stroke, a population the company estimates at 315,000 in the US. The company says its Layer 7 cortical surface array has been implanted in nearly 100 patients at several leading academic medical centers, that it has raised $180 million from investors, and that it has received FDA 510(k) clearance. Donoghue, a professor emeritus at Brown University, will advise on device design, signal decoding and clinical strategy as the company pushes ahead with a long-term implant study and commercial launch.

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.

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.

Brain-computer interfaces already help restore movement and communication, but attention and memory are a different problem: motor and language control rest on relatively localized, stable neural representations, while cognition is spread across brain networks and changes quickly. The review argues that neuromodulation, long used in neurological and psychiatric therapy and now applied causally to cognitive circuits, can complement decoding. Fold signal acquisition, algorithmic analysis and stimulation feedback into an adaptive closed loop, it suggests, and cognitive BCIs might restore mental function and become the basis for neurotherapeutics that monitor and shape cognition in real time. The article reports no new trial data.

Researchers at the Center for Neurotechnology at the University of Washington recorded from two male monkeys with high-density laminar microelectrode arrays and found that reaching-related activity in frontal motor cortex is unevenly distributed both across the cortical surface and with depth. Target-direction information varied sharply between neural populations, but the amount of task information a population carried predicted which populations shared similar temporal dynamics. The authors say the pattern should inform where electrodes are placed in future brain-computer interface implants.

An early feasibility study followed five people with spinal cord injury, each implanted with two Blackrock NeuroPort arrays in somatosensory cortex. Across two to 10 years of implantation, more than 168 million intracortical stimulation pulses were delivered over 27 combined implant-years without a serious adverse event or direct harm to electrode health. An average of 64% ± 13% of electrodes still evoked touch, including 60% after 10 years in one participant.

Researchers decoded 15 semantic categories from sEEG high-gamma activity in epilepsy patients at 29.8% accuracy (chance 6.7%), supporting semantic decoding feasibility for language BCIs. Preprint; not yet peer-reviewed.

Carnegie Mellon University researchers report a sensory-guided joint learning framework that pairs human motor learning with adaptive machine learning to train motor imagery BCI users. Across 31 BCI-naive participants, average online discrete accuracy was 86.0% in one dimension and 77.5% in two, with continuous control accuracy at 77.5% and 66.9% respectively; tactile guidance reduced how much users had to explore and accelerated neural adaptation, while sample reweighting kept decoder updates aligned with the learner's own trajectory. The authors frame the approach as a shift from passive calibration to active human-machine joint learning; the study appears in Nature Communications.

University of Washington Seattle researchers combine optogenetic stimulation with micro-ECoG in a semi-chronic implant, enabling stable causal connectivity measurement in macaques over weeks to years.

UC Riverside researchers propose a theoretical framework explaining why mesoscale temporal integration is often optimal for information representation in neural decoding.

Nova Southeastern University researchers combine intracranial ECoG network features with a support vector machine and Bayesian updating for real-time continuous seizure risk forecasting.

Researchers at Coherence Neuro Global, Inc. and other institutions have used implanted neural interfaces to track glioma growth in freely behaving mice over time. Across several mouse strains and glioma models, tumor progression went with elevated gamma-band activity in the tumor microenvironment, and machine learning models read tumor burden off the chronic recordings, with gamma trajectories predicting individual growth rates. The work was posted to bioRxiv on July 9, 2026, and has not been peer reviewed.

Harvard Medical School researchers report that spectral analysis of facial movements in rodents predicts both cortical activity and performance on brain-machine interface tasks, a possible biological basis for better neural prosthetic control. The work is a preprint and has not been peer reviewed.

Neurosom announced it has been granted a patent for a method that combines Bayesian statistical modeling with personalized EEG data and brain atlas models to achieve "super-resolution" in locating the origins of brain electrical activity, potentially improving the guidance of transcranial electrical stimulation (TES). The patent, titled "Method for Bayesian Super-Resolution of Electroencephalographic Source Analysis and Transcranial Electrical Stimulation" (US Pat. No. 12,663,866), was announced on July 9, 2026.

A preprint shows that functional ultrasound imaging (fUSI), read through an acoustically transparent cranial-window implant, reliably resolved multi-body-part and single-digit movement encoding in one participant's primary sensorimotor cortex, with maps consistent with classical somatotopy and single-trial decoding supported across sessions; analysis of key decoding voxels suggested different Brodmann areas encode single-digit movement differently. The researchers argue fUSI can map motor representations at submillimeter resolution, filling a key gap in humans between invasive electrophysiology and non-invasive blood-flow imaging; the work has not been peer reviewed.

Microsoft and INBRAIN Neuroelectronics said they will work together on AI-driven brain-computer interface therapeutics, pairing INBRAIN's graphene-based neural interfaces with Microsoft's cloud computing and data infrastructure to speed development of treatments for neurological disorders. INBRAIN, a Spanish neurotechnology company, says its system combines graphene electrodes with AI algorithms to read and modulate brain signals with high precision, on a platform it describes as bringing together neuroscience, bioelectronics and AI.

INBRAIN Neuroelectronics has announced a partnership with Microsoft to apply the software company's large language models and data analytics tools to make its brain-computer interfaces adapt in real time. INBRAIN builds graphene-electrode BCIs for neurological disorders, spanning invasive and non-invasive applications, and recently partnered with the Mayo Clinic to speed clinical development and commercialization of its BCI therapies, with investigator-led clinical research under way.

Barcelona-based INBRAIN Neuroelectronics said it will work with Microsoft on AI for neurology and brain-computer interfaces, combining INBRAIN's neuroscience, bioelectronics and AI work with Microsoft's cloud and AI infrastructure. INBRAIN's graphene-based neural interface is being developed as an adjunctive therapy for Parkinson's disease; it received FDA breakthrough device designation in 2023 and entered its first-in-human study in the fall of 2025.

The FDA cleared Universal Brain's UB ERP System on June 30, 2026, finding it substantially equivalent under submission number K253767 through the traditional 510(k) pathway. The agency classifies it as Non-Normalizing Quantitative Electroencephalograph Software, a Class II device under regulation 882.1400, with neurology as the assigned review panel. Event-related potentials are obtained by averaging EEG time-locked to stimuli; components such as P300 serve both clinical assessment and the classic paradigms behind noninvasive BCI spellers.