# Professional Summary This comprehensive review examines the integrated optimization of flexible sensors through coordinated advancement across four critical domains: materials science, structural design, hardware engineering, and algorithmic development. Rather than advancing these components in isolation, the article emphasizes a co-driven hierarchical optimization approach that leverages synergies between disciplines to enhance sensor performance. The review analyzes how novel materials enable improved sensitivity and durability, while innovative structural designs maximize mechanical flexibility and signal transduction. Hardware innovations in fabrication and integration are paired with sophisticated algorithms that process sensor data and improve accuracy. The research addresses why holistic optimization matters for developing next-generation flexible sensors suitable for wearable electronics, biomedical monitoring, and human-machine interfaces. By demonstrating how these four pillars interconnect, the article provides practitioners and researchers with a framework for more effective flexible sensor development, highlighting that breakthrough performance requires balanced progress across materials, structures, hardware components, and computational intelligence rather than focusing narrowly on single innovations.

China's regulatory framework for brain-computer interfaces (BCIs) is entering a period of accelerated approval. Within two months, two BCI products were admitted to the Special Approval Procedure for Innovative Medical Devices, following China's March approval of the world's first commercial invasive BCI device. The National Medical Products Administration (NMPA) released guiding principles in June establishing formal definitions and naming conventions for BCI medical devices for the first time. As of July 2026, four BCI products from domestic companies including NeuroSky, Zhiran Medical, and Tijie Medical have entered the expedited review process. NeuroSky's Implantable Brain-Computer Interface Hand Motor Function Compensation System achieved Class III certification in March, becoming the world's first approved invasive BCI medical device. Notably, NeuroSky completed approval in approximately 18 months—substantially faster than the typical 3-5 year timeline for traditional Class III devices and quicker than the 23-28 month average for innovative medical devices. This unprecedented regulatory momentum signals strong government commitment to advancing BCI technology commercialization in China's medical device sector.

Researchers have developed a scalable neural interface platform addressing a critical bottleneck in brain recording technology. The system integrates a custom-designed application-specific integrated circuit (ASIC) with 5376 simultaneous recording channels, each sampling at 20 kilosamples per second and achieving over 1.3 gigabits per second total data throughput. The ASIC incorporates in-pixel amplification, time-division multiplexed analog-to-digital converters, and on-chip stimulation capabilities, delivering exceptional signal quality with a noise floor of 5.5 microvolts root mean square. The researchers employed gold bump bonding for high-density integration between the flexible probe and rigid chip. Testing with a flexible microelectrocorticography array demonstrated the platform's capacity for high-resolution mapping of cortical field potentials in rat brains, with precise localization of evoked sensory responses. This advancement enables large-scale functional brain mapping and promises significant applications in brain-computer interfaces, neuroprosthetics, and understanding distributed neural circuits underlying sensorimotor processing, decision-making, and memory formation. The technology overcomes previous limitations in backend electronics for scaling neural recording systems.

Researchers have developed a comprehensive longitudinal EEG dataset from 24 stroke patients to advance motor imagery-based brain-computer interface (MI-BCI) applications in rehabilitation. The dataset captures three critical rehabilitation phases—pre-training, post-training, and follow-up—and introduces a novel "sixth finger" motor imagery paradigm alongside traditional affected-hand imagery tasks. The collected materials include raw EEG data, preprocessed signals, and clinical patient information. Preliminary analysis using classical machine learning algorithms—Common Spatial Pattern (CSP) combined with Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA)—achieved consistent classification accuracy of approximately 85-86% between the two MI paradigms. This dataset addresses significant gaps in current stroke rehabilitation research by providing multi-paradigm longitudinal data essential for optimizing MI-BCI systems and understanding neuroplasticity changes. By enabling closed-loop neural pathway establishment and facilitating high-efficiency MI-BCI system development, this resource promises to substantially enhance motor rehabilitation outcomes for stroke patients through evidence-based neural reorganization approaches.

Researchers have developed a double neural bypass (DNB), an innovative hybrid neuroprosthetic system designed to restore both immediate motor control and long-term sensory recovery in patients with complete spinal cord injuries. The system combines an intracortical brain-computer interface with targeted spinal cord and cortical neuromodulation, enabling patients to control their own hand movements in real time through brain signals while simultaneously promoting lasting neuroplasticity. The DNB utilizes recurrent artificial neural networks and reinforcement learning for precision grasp control, integrated with patterned spinal cord stimulation and activity-informed intracortical microstimulation—termed cortical mirroring—to enhance neural recovery. In a patient with chronic C4 sensory and C5 motor complete tetraplegia, the system successfully restored functional abilities including self-feeding and object manipulation, alongside significant and persistent improvements in elbow flexion and tactile sensation. This breakthrough demonstrates that combining sensorimotor neuroprosthetics with targeted brain and spinal neuromodulation offers substantial promise for restoring clinically meaningful function in severe paralysis, addressing upper limb movement recovery—the highest priority for spinal cord injury patients.

The 2024 NYC Neuromodulation Conference has released its collection of accepted abstracts for presentation at the event. This compilation serves as a comprehensive reference for researchers and professionals attending the conference, featuring synopses of accepted research submissions across neuromodulation topics. The conference organizers have established a structured process for abstract management, noting that presenting authors must follow specific guidelines when preparing their posters for display. Full abstracts will become publicly available following the conference, subject to author authorization. The organizers have implemented quality assurance measures, requesting that researchers contact the conference administration at contact@neuromodec.org if their submissions are missing from the list, lack assigned poster numbers, or if discrepancies exist regarding oral highlight designations. This systematic approach ensures transparency and proper documentation of the research being presented, while accommodating the varied presentation formats and author preferences within the neuromodulation field.

This research paper examines how different feedback modes in brain-computer interface (BCI) training affect learning outcomes and user performance. Brain-computer interfaces represent a significant advancement in neurotechnology, enabling direct communication between the brain and external devices through neural signal decoding. The study investigates various feedback presentation methods—such as visual, auditory, and proprioceptive feedback mechanisms—to determine which approaches optimize user learning and control accuracy during BCI training sessions. The research evaluates participant performance across different feedback conditions, measuring metrics including control accuracy, learning curves, and user engagement levels. Key findings demonstrate that feedback mode selection substantially influences training effectiveness, with certain feedback types producing superior learning trajectories compared to others. This work matters because optimizing BCI feedback mechanisms has direct implications for clinical applications, including stroke rehabilitation, assistive technologies for paralyzed patients, and brain-computer interface prosthetic control. By identifying the most effective feedback strategies, researchers can accelerate user adaptation to BCIs, reduce training time, and improve practical outcomes for patients requiring neurotechnological interventions. These insights contribute to advancing BCI technology toward more efficient, user-friendly clinical implementations.

Researchers have developed Neurobypass 1.0, an innovative 3D-printed hand orthosis designed to support rehabilitation in subacute post-stroke patients. This device represents a mechanically-driven approach that integrates neurocontrol technology to facilitate hand function recovery during the critical rehabilitation window following stroke. The orthosis combines 3D printing technology with neural control mechanisms, allowing patients to regain motor control and dexterity through targeted therapeutic intervention. By leveraging additive manufacturing, the device can be customized to individual patient anatomy and needs, potentially improving treatment outcomes and reducing rehabilitation duration. This advancement is significant because it addresses a major challenge in post-stroke recovery—restoring fine motor control in the hand—using accessible, scalable manufacturing techniques. The neurocontrolled orthosis bridges the gap between passive rehabilitation aids and fully active movement recovery, offering stroke survivors a practical tool for functional restoration during the subacute phase when neuroplasticity is most responsive to intervention. This represents meaningful progress in personalized rehabilitation medicine.

China's brain-computer interface sector is experiencing unprecedented regulatory acceleration. Two BCI products recently entered the Special Approval Procedure for Innovative Medical Devices within two months, following China's March approval of the world's first commercial invasive BCI device. On June 30, the National Medical Products Administration released guiding principles establishing formal definitions and naming conventions for BCI medical devices, marking the first official regulatory clarification in this category. As of July 2026, four domestic BCI products from companies including NeuroSky, Zhiran Medical, and Tijie Medical have entered the expedited review procedure. NeuroSky's Implantable Brain-Computer Interface Hand Motor Function Compensation System achieved Class III certification in March, becoming the world's first marketed invasive BCI with regulatory approval after just 18 months in the innovation channel—significantly faster than the typical 3-5 year approval timeline for traditional Class III devices and the 23-28 month average for innovative medical devices. This regulatory momentum signals substantive policy support and suggests the industry is entering a phase of batch approvals.

Researchers have developed an advanced neural recording and stimulation platform designed to overcome the technical limitations of existing large-scale brain interfaces. The innovation centers on a custom application-specific integrated circuit (ASIC) featuring 5376 simultaneous recording channels, each sampling at 20 kilosamples per second with a combined data throughput exceeding 1.3 gigabits per second. The ASIC incorporates integrated amplification, multiplexed analog-to-digital converters, and on-chip stimulation capabilities while maintaining exceptionally low noise levels of 5.5 microvolts RMS and minimal power consumption. The team employed gold bump bonding technology to achieve high-density integration between a flexible microelectrocorticography array and the rigid processing chip. Validation studies using rat brain recordings demonstrated successful high-resolution mapping of cortical field potentials and precise localization of evoked sensory responses. This scalable approach addresses a critical bottleneck in neural interface development and promises significant applications in brain-computer interfaces, neuroprosthetics, and comprehensive functional brain mapping studies.

This research addresses a critical challenge in mechanically flexible phased arrays: maintaining optimal beam patterns when the array deforms. The study presents a comprehensive framework for designing, modeling, and validating curvature-based shape sensing integrated into flexible phased arrays operating at 6 GHz. The researchers developed a physical model linking measured strain to local board curvature and experimentally validated an eight-element array configuration that achieved approximately 6 percent reconstruction accuracy while successfully recovering beam-steering performance at radii of curvature below 3.6 centimeters. The system employs a low-cost, eight-channel digitally controlled phase-shifter platform capable of 360-degree phase control. Unlike conventional feedback optimization approaches, this shape-sensing method recovers deformation from low-rate surface measurements independently of RF conditions without requiring feedback, making it a versatile sensing modality applicable beyond antenna systems. This advancement enables new applications for mechanically flexible phased arrays in previously impractical domains, including body-worn antennas, lightweight spaceborne systems, reconfigurable reflecting surfaces, and autonomous platforms, while simultaneously addressing the critical need to compensate for time-varying deformation effects in real-time operation.

Neuroengineering has emerged as a critical interdisciplinary field addressing the inherent complexity of the brain, which operates across multiple spatial and temporal scales from molecular and cellular levels to entire neural networks and behaviors. This editorial, compiled by researchers from the Neuroengineering Genoa Group and collaborating institutions, synthesizes contributions demonstrating how neuroengineering combines principles from neuroscience, engineering, computational modeling, and advanced technologies to comprehensively understand neural dynamics in both health and disease states. The field integrates experimental and computational approaches to develop innovative technologies that interact with the nervous system, including brain-machine interfaces, neuroprostheses, and targeted neuromodulation strategies. These applications hold significant promise for improving diagnosis, treatment, and rehabilitation of neurological disorders. The Research Topic encompasses diverse methodologies spanning advanced neuroimaging, electrophysiology, artificial intelligence, computational modeling, neural interfaces, and neuromodulation techniques. By bringing together multidisciplinary perspectives and complementary experimental models, these contributions illustrate how neuroengineering bridges fundamental understanding of neural function with practical development of nervous system-restoring technologies, advancing both basic neuroscience knowledge and clinical applications.

Brain-computer interfaces depend critically on preprocessing raw neural signals, yet this essential step remains manual, expert-dependent, and poorly reproducible across laboratories. Researchers have introduced EasyBCI, a two-phase large language model agent designed to automate preprocessing pipeline creation for six signal modalities. The system uses a Plan Agent to generate a text-only Data Fingerprint that protects raw data privacy while selecting literature-grounded operator sequences, followed by an Execution Agent that generates, runs, and self-corrects code until quality criteria are met. A quality-gated experience system retains validated strategies while automatically deprecating underperforming entries. Critically, domain experts maintain oversight at two decision gates—plan confirmation and repair exhaustion—ensuring human judgment prevents undetected errors that could invalidate downstream analyses. Systematic evaluation on EEG data demonstrated that all five EasyBCI backbones preserved more task-relevant linear separability than manually designed pipelines. The system successfully extended to five additional modalities spanning nearly three orders of magnitude in sampling rates, producing complete, reproducible pipelines with documented decision provenance. This work enables laboratories lacking dedicated preprocessing expertise to achieve auditable, reproducible neural signal analysis while illustrating broader design principles for AI agents in scientific domains where preprocessing decisions critically influence conclusions.

Intracortical microstimulation (ICMS) enables applications ranging from neuroprosthetics to causal circuit manipulations. However, the resolution, efficacy, and chronic stability of neuromodulation are often compromised by adverse tissue responses to the indwelling electrodes. Here we engineer ultraflexible stim-nanoelectronic threads (StimNETs) and demonstrate low activation threshold, high resolution, and chronically stable ICMS in awake, behaving mouse models. In vivo two-photon imaging reveals that StimNETs remain seamlessly integrated with the nervous tissue throughout chronic stimulation periods and elicit stable, focal neuronal activation at low currents of 2 μA. Importantly, StimNETs evoke longitudinally stable behavioral responses for over 8 months at a markedly low charge injection of 0.25 nC/phase. Quantified histological analyses show that chronic ICMS by StimNETs induces no neuronal degeneration or glial scarring. These results suggest that tissue-integrated electrodes provide a path for robust, long-lasting, spatially selective neuromodulation at low currents, which lessens risk of tissue damage or exacerbation of off-target side effects.

Implanted neural probes can detect weak discharges of neurons in the brain by piercing soft brain tissue, thus as important tools for brain science research, as well as diagnosis and treatment of brain diseases. However, the rigid neural probes, such as Utah arrays, Michigan probes, and metal microfilament electrodes, are mechanically unmatched with brain tissue and are prone to rejection and glial scarring after implantation, which leads to a significant degradation in the signal quality with the implantation time. In recent years, flexible neural electrodes are rapidly developed with less damage to biological tissues, excellent biocompatibility, and mechanical compliance to alleviate scarring. Among them, the mechanical modeling is important for the optimization of the structure and the implantation process.

Implanted neural interfaces are electronic devices that stimulate or record from neurons with the purpose of improving the quality of life of people who suffer from neural injury or disease. Devices have been designed to interact with neurons throughout the body to treat a growing variety of conditions. The development and use of implanted neural interfaces is increasing steadily and has shown great success, with implants lasting for years to decades and improving the health and quality of life of many patient populations. Despite these successes, implanted neural interfaces face a multitude of challenges to remain effective for the lifetime of their users. The devices are comprised of several electronic and mechanical components that each may be susceptible to failure. Furthermore, implanted neural interfaces, like any foreign body, will evoke an immune response. The immune response will differ for implants in the central nervous system and peripheral nervous system, as well as over time, ultimately resulting in encapsulation of the device. This review describes the challenges faced by developers of neural interface systems, particularly devices already in use in humans. The mechanical and technological failure modes of each component of an implant system is described. The acute and chronic reactions to devices in the peripheral and central nervous system and how they affect system performance are depicted. Further, physical challenges such as micro and macro movements are reviewed. The clinical implications of device failures are summarized and a guide for determining the severity of complication was developed and provided.

For several decades, the intracortical penetrating microelectrode arrays have been widely employed for the purpose of neural recording and stimulation in nervous system. However, the long-term application is limited due to the tissue reaction to the implanted electrode array. The tissue response includes the degeneration of nerve cells as well as the formation of dense glial sheath adjacent the implanted electrode called gliosis. The glial encapsulation deteriorates the capacity of electrodes to communicate with neurons by electrically isolating the devices from the neighboring brain regions. To examine and overcome these critical obstacles of microelectrode array for chronic applications, a number of studies have been performed to date including the reduction of electrode geometry, the use of flexible materials for electrode substrates, the pharmacological suppression of the cellular reaction, and the optimized surgical techniques. In this review, the studies to clarify the mechanism of the glial response in central nerve system (CNS) will be described and a variety of strategies for minimizing the glial responses in CNS will be examined.

Long-term stability of neural electrodes is difficult to achieve by relying on a single material strategy. A more feasible path is to establish multi-scale design principles that can explain and regulate interface responses. The material’s flexibility, electrochemical stability, protein adsorption behavior, immune recognition method, degree of glial scar, and spatial distribution of neurons jointly affect the final recording or stimulation performance. Organizing these factors along the lines of molecular mechanisms can move surface biofunctionalization from empirical coating optimization to verifiable, comparable, and iterable interface engineering strategies.

Flexible deep brain neural interfaces, as an important research direction in the field of neural engineering, have broad application prospects in areas such as neural signal detection, treatment of neurological diseases, and intelligent control systems. However, chronic inflammatory responses caused by long-term implantation and the resulting electrode failure seriously hinder the clinical development of this technology. This review systematically explores the long-term stability issues of flexible deep brain neural interfaces, with a focus on analyzing the synergistic optimization of electrode geometric morphology and implantation strategies in regulating inflammatory responses. Additionally, this paper delves into innovative strategies, such as passive enhancement of biocompatibility through electrode surface functionalization and active inhibition of inflammation through drug-controlled release systems, offering new technical paths to extend electrode lifespan. By integrating and reviewing existing innovative methods for deep brain flexible electrodes, this study provides an important theoretical foundation and technical guidance for the development of high-stability neural interface devices.

Neural interface devices are being developed for applications encompassing communication interfaces between prosthetics and patients and investigative tools for understanding complex neural circuitry. This work investigates encapsulation materials and strategies for chronic recording of neural electrical signals for intracortical electrodes. These devices could be used for brain-computer interfacing in applications related to the recording of volitional intent in conditions such as brainstem stroke, spinal cord injury, and locked-in syndrome.