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.

Researchers have developed an integrated approach combining circuit modeling and signal analysis to quantify crosstalk contamination in brain recordings, a critical issue affecting the reliability of neural data acquisition. Published in Nature Communications, this study by Porto Cruz, Zucchini, Vomero, and colleagues addresses how electrical signals from adjacent recording channels interfere with each other in microelectrode arrays used for neuroscience research. Using advanced circuit modeling techniques paired with sophisticated signal processing methods, the team characterized the magnitude and mechanisms of crosstalk across different electrode configurations and recording conditions. Their findings provide quantitative metrics for assessing signal integrity and contamination levels in brain recordings, enabling researchers to better interpret neural data and improve experimental design. This work is significant for neuroscience because accurate brain signal recording is fundamental to understanding neural circuits, developing brain-computer interfaces, and advancing neuroprosthetics. By establishing clear frameworks for identifying and mitigating crosstalk, the research enhances the quality and reliability of neurophysiological measurements, ultimately supporting more robust conclusions in brain research.

Brain implants, including deep brain stimulation (DBS) systems, brain-computer interfaces (BCIs), and speech neuroprostheses, are transitioning from experimental prototypes to clinical implementation, addressing significant unmet needs in movement disorders, paralysis, and speech impairment. Researchers at Alfaisal University and Sulaiman Alrajhi University analyzed these technologies through a shared architectural framework comprising sensing, decoding, stimulation output, power delivery, telemetry, and clinical validation. Recent regulatory milestones, notably the FDA's 2025 clearance of a cortical interface and expanding implanted-BCI trials, have accelerated progress. The review identifies common enabling advances including flexible electrodes, artificial intelligence-assisted decoding algorithms, and neuromorphic edge processing. However, three critical bottlenecks persist across all three technologies: long-term neural stability, understanding of neural coding mechanisms, and ensuring equitable patient access. The authors emphasize that translational progress is constrained by these shared challenges rather than technology-specific obstacles, and stress the need for governance frameworks to support continued engineering advancement alongside clinical deployment.

Endovascular brain–computer interface (EBCI) offers an integrated solution for the early diagnosis and neuroregulatory treatment of AD patients, with minimal invasiveness. This review comprehensively examines the safety and feasibility of EBCI for AD patients, focusing on 3 major application areas: early diagnosis, deep brain stimulation targeting specific brain regions, such as the fornix and the basal nuclei of Meynert, and the use of external neurofeedback devices. Furthermore, we explore future development trends in this field, including miniaturization, integration, and the exploration of deep brain regions.

Early results are promising, but clinical data remain scarce. Further research is needed to optimize signal processing, enhance electrode biocompatibility, and refine endovascular procedures for broader clinical applications.

Neural electrodes capable of precise interrogation and biofunctional regulation—encompassing both long-term electrophysiological signal recording and tunable control of neuronal or pathological cell behavior via surface biofunctionalization—hold significant promise for advancing the diagnosis and treatment of neurological disorders. A critical challenge lies in mitigating the immune-mediated foreign body response, which often leads to glial scar formation that encapsulates implants like “cement,” severely compromising functionality. To address this, we develop a benzyloxycarbonyl (Cbz)-substituted poly(ornithine-alt-glycine) (OGCbz) interface, which inherently resists biofouling and immunogenic rejection without degrading electrical performance.

These results demonstrate that endovascular electrodes may access non-superficial brain structures and evoke reproducible cortical responses without open neurosurgery. This work helps establish a foundational framework for endovascular neuromodulation and supports further investigation of its potential for future closed-loop and network-level neuromodulation research.

# Professional Summary This comprehensive review by Mavrych, Bolgova, Alhamd, and Alissa examines the current state of implantable neurotechnology, tracing the evolution from deep brain stimulation to advanced brain-computer interfaces. Published in Frontiers in Neuroscience, the article documents progress in neuromodulation techniques and their clinical applications. The research synthesizes findings on how implantable devices are increasingly bridging the gap between traditional therapeutic approaches like deep brain stimulation and next-generation technologies such as neuromorphic computing and speech neuroprosthesis systems. The work highlights the technological advancements enabling direct neural communication and control, demonstrating significant potential for treating neurological conditions and restoring lost functions. By mapping the trajectory from established deep brain stimulation protocols to cutting-edge brain-computer interfaces, this review underscores the accelerating pace of innovation in implantable neurotechnology. The findings matter considerably for the medical device industry, neurology specialists, and patients, as they indicate expanding therapeutic possibilities for movement disorders, neurological disabilities, and conditions affecting communication and motor control.

Chinese researchers have developed a breakthrough multifunctional electrode coating to overcome critical obstacles in long-term brain-computer interface implantation. Led by Zhang Wei at the Technical Institute of Physics and Chemistry under the Chinese Academy of Sciences, in collaboration with Beijing Tiantan Hospital, Shanghai Institute of Microsystem and Information Technology, and NeuroXess, the team created a cationic alternating peptide coating that addresses signal degradation and unsafe device removal. The coating maintains electrode flexibility and conductivity while providing antibacterial, anti-protein adsorption, and immune-rejection-inhibiting properties. In 300-day animal trials, coated electrodes sustained stable recording channels with neural signals consistently above 155 microvolts, whereas bare electrodes experienced severe signal loss after 90 days and near-complete failure by 300 days. The coated electrodes also required significantly lower current levels to trigger neural responses and could be safely removed without causing brain damage. This innovation substantially advances BCI clinical viability by enabling long-term electrode stability and safe replacement, addressing the chronic inflammation and scar tissue formation that have previously limited implant longevity to approximately three months.