China's National Medical Products Administration has issued the world's first binding technical standard for artificial intelligence systems processing brainwave data in medical-grade brain-computer interface devices, establishing uniform requirements across five stages of electroencephalogram data handling: collection, processing, labeling, storage, and access. The standard, effective September 1, 2027, applies specifically to AI-powered BCI medical devices that translate recorded brainwaves into physical commands such as controlling robotic limbs or powered wheelchairs. Unlike the FDA and European regulators, who have made limited progress on this issue, China has established concrete guidelines for managing this sensitive neural data. EEG recordings capture electrical brain activity and encode not only intended movements but also cognitive states, emotional patterns, and mental health information, raising significant privacy concerns. The standard addresses critical challenges in AI-BCI development, where training data quality directly impacts system performance. This regulatory initiative is significant because neural data is extremely sensitive and highly individual-specific, yet crucial for developing effective AI models that decode patient intentions from brain signals.

Quantum sensing technologies promise revolutionary advances in biomedical measurement by exploiting quantum mechanical principles to achieve unprecedented sensitivity beyond classical limits. This article proposes a generational framework organizing quantum biosensors by their utilization of quantum resources. First-generation devices use discrete energy levels with classical scaling, while second-generation sensors exploit quantum coherence to reach the standard quantum limit. Third-generation architectures employ entanglement and spin squeezing for Heisenberg-limited precision. The emerging fourth generation integrates quantum sensing with quantum learning and variational circuits, enabling adaptive inference within the quantum domain itself. The authors introduce bandwidth-matching analysis to pair neural signal hierarchies with platform response capabilities, classify clinical devices by precision-scaling class and sensor-tissue proximity, and outline a technological roadmap toward learning-integrated sensor networks. This framework identifies critical bottlenecks in translating quantum sensors to clinical settings and charts the transition from measuring physical observables to extracting structured biological information using quantum-enhanced intelligence, addressing the fundamental challenge of moving quantum sensing from laboratory demonstrations to practical biomedical applications.

The Fall 2026 ICD-10 Coordination and Maintenance Committee Update addresses developments in the International Classification of Diseases, 10th Revision coding system, which is essential for healthcare billing, research, and epidemiological tracking. The committee convenes to review proposed code modifications, expansions, and clarifications that will take effect in the fall of 2026. These updates reflect emerging medical conditions, evolving clinical practices, and the need for greater diagnostic specificity in healthcare documentation. The ICD-10 system, maintained through this coordinated process, ensures that healthcare providers, insurers, and public health agencies use standardized, current terminology for disease classification. Regular maintenance committee updates are vital for keeping the coding system aligned with advances in medicine and epidemiology, thereby improving data accuracy, reimbursement precision, and public health surveillance capabilities across healthcare systems.

Researchers have developed a novel technique using direct laser carbonization of parylene-C to create advanced microelectrodes for biomedical applications. Parylene-C, a biocompatible polymer coating, was processed through laser treatment to convert it into conductive carbon material, enabling the fabrication of high-performance microelectrodes. This approach combines the advantages of parylene-C's excellent biocompatibility and conformal coating properties with the enhanced electrical conductivity of carbonized material. The resulting microelectrodes demonstrate improved performance characteristics for recording neural and physiological signals. This innovation is significant because it offers a promising pathway for developing minimally invasive neural interfaces and biosensors with superior electrical properties while maintaining biocompatibility. The technique potentially streamlines microelectrode manufacturing compared to conventional methods, reducing complexity and cost while improving device quality. These microelectrodes could enable advances in brain-computer interfaces, neural monitoring systems, and other bioelectronic medical applications requiring precise signal acquisition from biological tissues.

Brain-computer interfaces represent a transformative technology requiring stable neural-electrode interfaces for long-term clinical viability. Researchers at the CAS Technical Institute of Physics and Chemistry have developed a breakthrough coating based on alternating cationic peptides for neural electrodes that simultaneously enhances biocompatibility, provides broad-spectrum antibacterial activity, resists protein and ion adhesion, and suppresses foreign body responses while preserving electrochemical performance. The peptide modification material, compatible with diverse substrates including silicon, gold, polyimide, and medical-grade polyurethane, forms an ultrathin layer through covalent grafting that functions via moderate high-potential activity and hydrogen-bonded hydration barriers. Extended animal testing of modified flexible electrodes implanted in motor cortex demonstrated remarkable stability over 300 days, with no degradation in functional channels and neuronal action potentials maintaining levels above 155 microvolts. Most significantly, neuromodulation efficiency improved dramatically, requiring only two microamperes to trigger motor responses—a 50-fold enhancement compared to conventional electrodes. The coating's anti-biofouling properties enabled intact electrode extraction without tissue damage, positioning this technology as a universal solution for clinical-grade BCIs capable of ultra-long-term functionality and safe replacement.

High-fidelity intracranial electrode arrays for recording and stimulating brain activity have facilitated major advances in the treatment of neurological conditions over the past decade. Traditional arrays require direct implantation into the brain via open craniotomy, which can lead to inflammatory tissue responses, necessitating development of minimally invasive approaches that avoid brain trauma. Here we demonstrate the feasibility of chronically recording brain activity from within a vein using a passive stent-electrode recording array (stentrode). We achieved implantation into a superficial cortical vein overlying the motor cortex via catheter angiography and demonstrate neural recordings in freely moving sheep for up to 190 d. Spectral content and bandwidth of vascular electrocorticography were comparable to those of recordings from epidural surface arrays. Venous internal lumen patency was maintained for the duration of implantation. Stentrodes may have wide ranging applications as a neural interface for treatment of a range of neurological conditions.

# Professional Summary: Surgical Treatment of Epilepsy This document provides comprehensive clinical notes on the surgical management of epilepsy, addressing treatment approaches for patients with drug-resistant seizures. The content encompasses the evaluation, selection, and execution of various surgical techniques designed to eliminate or significantly reduce seizure activity when medical management fails. The document covers essential pre-operative assessment protocols, including neuroimaging, electroencephalography, and neuropsychological testing to identify seizure foci and determine surgical candidacy. Multiple surgical approaches are detailed, ranging from focal resections targeting specific epileptogenic zones to more extensive procedures such as temporal lobe resection and corpus callosotomy. The materials also address palliative neuromodulation options for patients unsuitable for resective surgery. By systematically presenting diagnostic criteria, surgical indications, and expected outcomes, this resource serves as a practical guide for healthcare professionals involved in evaluating and managing candidates for epilepsy surgery, ultimately improving seizure control and quality of life for patients with intractable epilepsy.

Endovascular neural interfaces (ENIs) represent a significant advancement in minimally invasive neural recording and stimulation technology. These devices are implanted within blood vessels adjacent to neural tissue, enabling neural activity monitoring through the vessel wall while eliminating the need for direct brain implantation. Developed by researchers at Imperial College London and the University of Melbourne, ENIs leverage angiographic procedures similar to cardiovascular interventions, substantially reducing the risk of neural tissue damage compared to traditional electrode implants. Early iterations adapted guide wires and catheters from cardiovascular procedures but were limited to acute applications. The field achieved a major breakthrough in 2016 when stent technology was integrated with electrode arrays, demonstrated by Synchron's Stentrode, which has successfully functioned as a chronic brain-computer interface for motor cortex recording. Future development will benefit from adopting cardiovascular engineering principles to enhance device miniaturization, improve stimulation capabilities, advance wireless communication, and ensure long-term safety. These improvements could transform neurological disorder treatment, providing high spatial resolution recording and stimulation capabilities through accessible, minimally invasive procedures.

Subsense has developed a non-surgical brain-computer interface platform utilizing two types of nanoparticles that function as neural antennas, enabling wireless reading and writing of neural signals. The company recently secured $10 million in additional seed funding to advance its technology. Initially targeting neurological conditions including Parkinson's disease, epilepsy, and other disorders affecting over three billion potential patients worldwide, Subsense aims to provide safe neuro-modulation and neural reading solutions. Following clinical validation, the platform could enable revolutionary applications including novel human-technology communication, neural-level human-AI interaction, and cognitive enhancement. The company has established a new laboratory in Palo Alto and initiated early regulatory engagement with the FDA to guide its development pathway.

Surgical brain-computer interfaces (BCIs) were an important step forward for conditions with few treatment options. Even if this technology was only for a select few, it proved that neural interfaces work. Those early systems also shaped what the field came to accept as normal. We are now in a position to question those assumptions—and we should.

Non-surgical implantable BCI is increasingly capable of matching surgical performance while transforming the risk-benefit equation, and it is now the most credible path along all three dimensions simultaneously.

Vascular scaffolds are fundamental devices in treating vascular occlusions, aneurysms, and hemodialysis access. However, their long-term efficacy is often compromised by 2 major pathophysiological responses: acute thrombosis and intimal hyperplasia, underscoring the need for effective antithrombotic treatment and intensive surveillance. This review highlights the emerging approaches used to address such challenges in vascular scaffolds: surface biofunctionalization and intelligent monitoring systems. We first introduce the leading biodegradable elastic polymers for vascular scaffolds, followed by a comprehensive overview of surface biofunctionalization techniques for preventing thrombosis and promoting endothelialization. The review further explores the cutting-edge advances in integrating flexible bioelectronics with cardiovascular implants for intelligent real-time monitoring of hemodynamics, thrombosis, and restenosis. It concludes with a discussion of the remaining challenges and future perspectives, thereby promoting the development of more effective cardiovascular therapies and their clinical applications.

Brain-computer interfaces (BCIs) can provide patients with severe motor disorders with a new way to bypass damaged nerve pathways and control external devices by decoding the nerve activity of the motor cortex. Current research has made progress in continuous movement, multi-degree-of-freedom control and sensory feedback, but long-term stability, control accuracy and clinical safety are still limited. This paper analyses the signal acquisition characteristics of invasive, non-invasive and intravascular BCIs, sorts out the signal preprocessing, feature extraction and neural decoding processes, and summarizes the generation methods of continuous, discrete and multi-degree-of-freedom robotic arm instructions. Analysis shows that the collaborative optimization of signal quality, decoding algorithm and shared control strategy is the key to improving the naturalness and reliability of robotic arm operation. This article provides a systematic reference for the design and research of the BCI robotic arm system. Future research should focus on developing stable neural interfaces, adaptive individual decoding, computer vision assistance and two-way sensory feedback to promote their long-term clinical and daily applications.

Despite being so ‘natural’, the generation of movement is a very complex task. Depending on the goal, the brain computes current and previously stored information to generate instructions and commands that are transformed into movement. This transformation is achieved at the neuromuscular junction, where a motor neuron synapses on a muscle governing its state of contraction. Therefore, to understand how purposeful movements are generated we need to understand how the nervous system is organised and how different regions communicate to control the correct sequence of contraction of hundreds of muscles that will produce the appropriate movement.

Movement is a central behavior of daily living; thus lost or compromised movement due to disease, injury, or amputation causes enormous loss of productivity and quality of life. While prosthetics have evolved enormously over the years, restoring natural sensorimotor (SM) control via a prosthesis is a difficult problem which neuroengineering has yet to solve. With a focus on upper limb prosthetics, this perspective article discusses the neurophysiology of motor control under healthy conditions and after amputation, the development of upper limb prostheses from early generations to current state-of-the art sensorimotor neuroprostheses, and how postinjury changes could complicate prosthetic control. Current challenges and future development of smart sensorimotor neuroprostheses are also discussed.

Researchers at the University of Pittsburgh have achieved a landmark breakthrough in brain-computer interface technology, enabling a paralyzed volunteer to control a robotic arm with his mind while experiencing tactile sensations. Nathan Copeland, who sustained a spinal cord injury in 2004, received implanted electrodes in both his motor and sensory cortex, making him the first person worldwide to possess this dual capability. The bidirectional interface allows signals to travel both ways—transmitting motor commands to the artificial limb while simultaneously delivering touch feedback to the brain. This advancement demonstrates that restoring sensory perception significantly enhances prosthetic functionality compared to vision-alone control, offering tremendous potential for improving quality of life for quadriplegic individuals.

This article examines implant-assisted and bioelectronic therapeutic approaches for treating Meniere's disease, a chronic inner ear disorder characterized by vertigo, hearing loss, and tinnitus. The document reviews emerging implantable technologies and bioelectronic interventions designed to manage symptoms and improve patient outcomes when conventional treatments prove insufficient. These advanced therapies represent a paradigm shift from traditional pharmaceutical and surgical approaches, offering targeted delivery mechanisms and neuromodulation techniques. The significance of this research lies in addressing the substantial clinical burden of Meniere's disease, which severely impacts quality of life and functional capacity in affected patients. By exploring implant-based solutions and bioelectronic systems, the article highlights promising alternatives for patients with refractory symptoms who have exhausted standard treatment options. These innovations demonstrate the potential to provide sustained symptom relief, restore vestibular function, and preserve hearing through precisely engineered medical devices. The integration of bioelectronic technologies with traditional implant platforms represents an important frontier in otologic medicine, offering hope for improved therapeutic outcomes in this challenging neurological condition.

Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices such as artificial limbs or computers by translating neural signals into actionable commands. The technology operates by acquiring brain signals through sensors, analyzing distinctive brainwave patterns including Delta, Theta, Alpha, Beta, and Gamma frequencies, and converting these signals into electrical outputs. BCIs facilitate bidirectional communication, allowing users to control external devices based on their brain activity while emerging applications explore brain-to-brain communication technology. Three primary BCI categories exist based on electrode implantation: invasive BCIs, which involve surgically placing electrodes directly on grey matter to achieve high-quality signal detection but carry risks of scar tissue formation and immune rejection; partially invasive BCIs, implanted within the skull but outside grey matter, offering higher resolution than non-invasive options with reduced complications; and non-invasive BCIs using scalp-placed sensors. Invasive BCIs have demonstrated significant clinical utility for paralyzed patients, enabling control of artificial limbs, environmental devices, and computer interfaces. Understanding these distinctions is crucial for selecting appropriate BCI technology based on signal quality requirements, medical safety considerations, and specific patient needs in therapeutic and assistive applications.

Researchers from Beijing Institute of Petrochemical Technology, Tiangong University, and Xiangyu Medical have developed FRFNet, a fatigue-robust brain–muscle fusion network designed to improve movement intention recognition for rehabilitation systems and assistive devices. The system addresses a critical challenge in combining electroencephalography (EEG) and surface electromyography (sEMG) signals: muscle fatigue degrades EMG signal quality during extended rehabilitation tasks, compromising the reliability of hybrid brain–computer interfaces. FRFNet integrates a multiscale adaptive temporal convolutional network for EEG encoding with a dual-path fatigue disentanglement encoder for EMG analysis, coupled with a dynamic weighted adaptive gating fusion mechanism. This architecture enables the system to separate fatigue-invariant action features from fatigue-sensitive signal degradation while automatically adjusting the relative contributions of EEG and EMG based on modality quality. Testing on a public multimodal dataset with synthetic fatigue-induced EMG degradation levels from ten to ninety percent demonstrated superior performance, achieving sixty percent accuracy at maximum degradation under within-subject protocols and fifty-seven percent under rigorous leave-one-subject-out evaluation, substantially outperforming existing fusion methods including E2FNet, DCA Fusion, and DMEFNet. This advancement enhances the practical applicability of hybrid EEG–EMG systems for real-world clinical rehabilitation scenarios.

This research article investigates how the lateral entorhinal cortex (LEC) orchestrates distributed learning across multiple cortical regions through gamma-frequency synchronization. The study reveals a critical mechanism by which the LEC coordinates neural activity across distant brain areas during learning tasks, enabling efficient information processing and memory formation. Using electrophysiological recordings and analysis of gamma oscillations—high-frequency neural rhythms associated with cognitive processing—the researchers demonstrated that LEC-mediated gamma-synchrony facilitates communication between cortical networks. The findings indicate that the LEC acts as a central hub that synchronizes gamma oscillations across cortical areas, allowing these regions to engage in coordinated learning. This distributed cortical learning mechanism has significant implications for understanding how the brain integrates information across multiple processing streams and could inform future research on learning disorders and cognitive dysfunction. The research published in Nature Communications provides novel insights into the neural basis of learning and represents an important advancement in understanding cortical network dynamics and information integration during cognitive tasks.