United States

Researchers from the University of Pittsburgh and Carnegie Mellon University tested whether changes in attentional load affected intracortical BCI control in two participants with tetraplegia. Participants moved and clicked a 2D cursor while completing a working memory task. EEG measures and difficulty ratings suggested increased attentional load during dual-tasking, but BCI performance remained largely stable across the dual-task conditions. P2 showed small but significant increases in trial completion time and normalized path length under mild compared with moderate load. The authors suggested that differences in signal quality between participants may have influenced the results; this remains an interpretation rather than an established causal relationship.

University of Melbourne researchers studied one participant implanted with a Stentrode endovascular brain-computer interface who attempted to handwrite English characters. They classified the attempts offline and online using neural signal trajectories over time. Across four online sessions, each containing 20-30 trials per character and 80-180 rest trials, the best-performing 3-class model achieved a mean rest recall of 87%, character recall of 65%, and character precision of 59%. Adjusting all trials to the same duration significantly improved classification compared with raw trials. The findings support further investigation of temporal information for motor decoding, but come from one participant and a small number of classes.

A University at Buffalo author evaluated an assistive communication interface combining a BCI with eye tracking. Gaze narrows the target area and brain signals confirm selection. The study reports faster selection than BCI alone, better control than eye tracking alone, and fewer selection errors and corrections.

A registered study that is not yet recruiting will compare a wearable brain-computer interface communication device with the eye-tracking devices already used by people with ALS. It plans to enroll 30 adults who use Tobii Dynavox or Smartbox eye-tracking devices. Each participant will try three options at home over 12 weeks: their own eye-tracking device and Cognixion ONE with either a head-worn augmented-reality display or a screen-based display. The main measure is the time participants voluntarily choose to use each option. Additional measures include communication speed, accuracy, and caregiver burden. No results are available. Cognixion ONE is investigational and is not FDA-approved or cleared for any medical use.

Precision Neuroscience announced a human central nervous system atlas combining high-resolution surface electrical recordings from 70 patients at 12 academic medical centers. It spans multiple cortical regions as well as brainstem and spinal cord structures. The company says the atlas includes more than 180 hours of synchronized neural and behavioral data and demonstrates motor decoding, spoken-word classification, and classification of eight facial expressions. The manuscript describes a 70-patient cohort; the broader total of patients implanted with the company's system had surpassed 100 by the announcement. These are distinct counts. The findings were disclosed by the company in connection with a preprint that has not been peer reviewed.

Rats implanted with microwire arrays showed an exponential-like fall in intracortical microstimulation (ICMS) perception thresholds from weeks 5 to 12, then held near a fitted asymptote of about 1.1 nC/ph per electrode across 10 stimulating electrodes through week 30 in animals that retained detectable percepts. Correct responses to the conditioned stimulus stabilized at roughly 90%. Neither the arrays' electrophysiological nor their electrochemical properties explained the threshold trend across animals. Gene expression in implanted tissue showed elevated Gfap and Cd68 alongside reduced NeuN and Bdnf, consistent with earlier neuroinflammation findings. Two animals lost sensation over the 30-week period.

A stretchable EEG platform called MindStretcH maintained stable scalp contact through hair over prolonged use, with no measurable drop in recording quality across repeated online brain-computer interface sessions, researchers at The University of Texas at Austin reported. The system combines a kirigami-patterned elastomer mesh, liquid-metal interconnects and detachable hydrogel electrodes. The abstract does not disclose participant numbers or session duration.

Can watching movements help stroke survivors retrain their arms? This scoping review screened 386 records and included 22 studies of upper-limb action observation therapy that measured EEG. Mu and beta rhythms over sensorimotor areas consistently desynchronized while patients watched movements, a sign that motor-related brain regions were engaged, and multi-week training produced sustained increases that often coincided with better upper-limb function scores. The authors see promise in BCI-based closed-loop therapy that uses EEG to adjust training in real time, but note that widely varying protocols make results hard to compare across studies.

Precision Neuroscience closed an oversubscribed $250 million Series D co-led by Pershing Square Inc., the Ackman Oxman Institute and an undisclosed life sciences fund, bringing its total raised since 2021 to $430 million. The company says it has completed more than 100 patient procedures at 18 medical institutions and holds FDA clearance for its Layer 7 Cortical Interface, a thin-film electrode array placed on the brain surface. It plans to use the money to expand clinical work, move Layer 7 through the next stages of FDA review and build out commercialization.

A participant with severe upper-limb paralysis caused by amyotrophic lateral sclerosis (ALS) wore an endovascular stent-electrode array and a scalp EEG cap at the same time, in the same session, while attempting ankle flexion and extension. Both recordings changed markedly during attempted movement, but the stent array showed stronger per-channel motor modulation and was unaffected by skull attenuation. Scalp EEG was more susceptible to eye blinks and jaw-muscle activity, while the stent array picked up prominent cardiac signals. Neither method reliably distinguished left from right ankle movement, leaving spatial localization an open problem.

In three participants with tetraplegia imagining object manipulation, neural activity across several brain regions represented the configuration of the whole hand during imagined grasps. The posterior parietal cortex simultaneously encoded grasp and object properties, largely through distinct neuronal subpopulations. Grasp-related activity varied with motor planning versus imagined execution and with object presence versus absence; some regions also represented object shape during planning. The authors suggest that separating grasp and object information could support more stable BCI grasp control. However, the motor imagery task does not establish restored hand function or improved robotic grasp performance. The study is a preprint that has not been peer reviewed.

A brain-computer interface can read neural activity tied to movement, while a powered exoskeleton supplies the force needed to carry it out. But EEG is noisy, muscle signals shift with fatigue and recovery, and the right level of assistance depends heavily on the user and the task, so AI's main value lies in handling several changing signals at once rather than relying on a single fixed input. The review surveys how EEG, EMG and mechanical sensing are combined for exoskeleton control and contrasts two applications with very different goals: stroke rehabilitation and healthy users. The author argues that higher classification accuracy alone is not enough, since latency, calibration, fatigue, uncertainty and physical safety also determine whether a system is truly usable.

During removal of a right parafalcine meningioma, surgeons placed four Layer 7 μECoG arrays, 4,096 electrodes in total, on either side of the central sulcus and recorded somatosensory evoked potentials under 5 contralateral stimulation conditions. Using a 2 MΩ impedance cutoff, 3,739 channels were usable, a 91.3% yield; phase-reversal latencies were 19, 21, 25, 26 and 25 ms, consistent with the standard intraoperative mapping performed in the same operation. The researchers caution that the setup provides dense spatial sampling rather than submillimeter physiological resolution: measured responses were correlated across roughly 3–4 mm of cortex.

After days of practicing a brain-computer interface with adaptive decoders, rhesus macaques needed significantly fewer motor cortex neurons to reach 80% task classification accuracy than in early sessions (p = 9.8 x 10^-4). Fixed decoders produced no such compaction. A neural network model suggests the adaptive decoders directly contribute to these more compact representations; the authors propose this may be because the decoders alter the error information used for credit assignment. The finding implies that assistive algorithms do not merely boost immediate BCI performance but actively reshape how the brain learns over time -- a consideration for real-world decoder design. The study used 128-channel microwire arrays in motor and premotor cortices of two monkeys and has not been tested in humans.

Researchers at the University of California, San Francisco (UCSF), a public research university known for its neuroscience work, had two Parkinson's disease patients train at home with a brain-computer interface (BCI) airplane-simulator game, learning to down-regulate cortical beta activity and thereby control the intensity of their own deep brain stimulation (DBS). The work, posted as a preprint on medRxiv on August 17, 2026, points to BCI applications in neuromodulation and to more personalized treatment for Parkinson's and other conditions; its conclusions have not yet been peer reviewed.

Pascall Systems, Inc. received U.S. Food and Drug Administration (FDA) 510(k) clearance for its Pascall Sensor Unit (PSU1) on August 19, 2026, with the agency finding the device substantially equivalent. The device is a Class II medical device under regulation number 882.1400, classification name Non-Normalizing Quantitative Electroencephalograph Software.

On May 8, 2026, Apple notified the European Commission under Article 14 of the Digital Markets Act that it would, through a subsidiary, acquire certain assets of Sonera and offer jobs to and hire certain Sonera employees. The deal value was not disclosed and neither party has announced it; May 8 is the filing date, and no closing date has been made public. The Commission describes Sonera as a company developing chip-scale magnetic sensors. Founded in 2018 in Berkeley, California, as a University of California, Berkeley spinout, Sonera builds acoustically driven ferromagnetic resonance magnetic sensors that operate at room temperature and has raised about $20 million.

Chronic stroke patients who used an at-home brain-computer interface (BCI) therapy system gained a mean 6.0 points on the Upper Extremity Fugl-Meyer Assessment after 12 weeks, versus 1.5 points for those on a home exercise program. Response rates were 55.5% and 9.6%, for a number needed to treat of 2.2. The trial also exposed the dropout problem in home-based studies: 17 of 42 control participants withdrew because they were dissatisfied with their group assignment.

A wireless implantable neural interface, the Brain Interchange (BIC), paired with artifact-removal algorithms captured about 82% of the high-frequency oscillations (HFOs) that a clinical-grade amplifier detected across 24-hour intracranial EEG recordings in 10 patients with drug-resistant epilepsy, according to a preprint. Using those HFOs, the system localized the seizure onset zone with 84% sensitivity and 90% specificity, comparable to clinical equipment. The authors present the work as a translational framework for chronic tracking of epileptogenic networks and for future biomarker-guided adaptive neuromodulation.

A team in France and the United States has released NETBCI, a longitudinal multimodal dataset pairing magnetoencephalography (MEG) and electroencephalography (EEG) from 19 healthy subjects across 4 sessions performed on 4 different days, built to study how brain networks reorganize during brain-computer interface training. Controlling a BCI remains a learned skill that a non-negligible proportion of users never acquire even after several sessions, the authors write, and the causes of that inter-individual variability remain an open question. Each session comprises 2 eyes-open resting-state recordings of 3 minutes each plus 6 runs in which participants either sustained right-hand motor imagery or stayed at rest to control the position of a virtual cursor, and the release also includes anonymized MRI scans and behavioral scores; the authors say they hope the sample size and range of modalities will support analyses beyond brain network reorganization.

A team from the University of Utah, Kangwon National University and the University of Georgia describes a silicon-free route to neural microelectrode arrays: soft polymeric 3D microneedles are printed by two-photon polymerization, then turned into electrodes by injecting liquid gallium into the hollow channels. A newly defined retention number predicts whether the gallium stays put under physiological conditions. Coating the surface with gold nanoparticles and PEDOT doped with tetrafluoroborate cut impedance by roughly 3 orders of magnitude, and in vivo recordings in an invertebrate model captured neural spikes with no gallium leakage. The study was published in ACS Sensors on August 31, 2026.

Researchers at the University of Colorado Boulder have built ERP-XTTN, a cross-attention model that classifies event-related potentials (ERPs) in users it has never seen, with no per-user calibration. Across three public datasets and eight ERP components, it trailed the best baseline by 0.025 AUROC on average using only three channels. The work appeared in the Journal of Neural Engineering.