Cyborg and bionic systems (Washington, D.C.)最新文献

{"title":"Integrated Piezoelectric Vibration and In Situ Force Sensing for Low-Trauma Tissue Penetration.","authors":"Bingze He, Yao Guo, Guangzhong Yang","doi":"10.34133/cbsystems.0417","DOIUrl":"https://doi.org/10.34133/cbsystems.0417","url":null,"abstract":"<p><p>Precision-controlled microscale manipulation tasks-including neural probe implantation, ophthalmic surgery, and cell membrane puncture-often involve minimally invasive membrane penetration techniques with real-time force feedback to minimize tissue trauma. This imposes rigorous design requirements on the corresponding miniaturized instruments with robotic assistance. This paper proposes an integrated piezoelectric module (IPEM) that combines high-frequency vibration-assisted penetration with real-time in situ force sensing. The IPEM features a compact piezoelectric actuator integrated with a central tungsten probe, generating axial micro-vibration (4,652 Hz) to enable smooth tissue penetration while simultaneously measuring contact and penetration forces via the piezoelectric effect. Extensive experiments were conducted to validate the effectiveness and efficacy of the proposed IPEM. Both static and dynamic force-sensing tests demonstrate the linearity, sensitivity (9.3 mV/mN), and accuracy (mean absolute error < 0.3 mN, mean absolute percentage error < 1%) of the embedded sensing unit. In gelatin phantom tests, the module reduced puncture and insertion forces upon activation of vibration. In vivo experiments in mouse brains further confirmed that the system could reduce penetration resistance (from an average of 11.67 mN without vibration to 7.8 mN with vibration, decreased by 33%) through the pia mater and accurately mimic the electrode implantation-detachment sequence, leaving a flexible electrode embedded with minimal trauma. This work establishes a new paradigm for smart surgical instruments by integrating a compact actuator-sensor design with real-time in situ force feedback capabilities, with immediate applications in brain-machine interfaces and microsurgical robotics.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0417"},"PeriodicalIF":18.1,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12538090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145350342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroanatomy-Informed Brain-Machine Hybrid Intelligence for Robust Acoustic Target Detection.","authors":"Jianting Shi, Jiaqi Wang, Weijie Fei, Aberham Genetu Feleke, Luzheng Bi","doi":"10.34133/cbsystems.0438","DOIUrl":"10.34133/cbsystems.0438","url":null,"abstract":"<p><p>Sound target detection (STD) plays a critical role in modern acoustic sensing systems. However, existing automated STD methods show poor robustness and limited generalization, especially under low signal-to-noise ratio (SNR) conditions or when processing previously unencountered sound categories. To overcome these limitations, we first propose a brain-computer interface (BCI)-based STD method that utilizes neural responses to auditory stimuli. Our approach features the Triple-Region Spatiotemporal Dynamics Attention Network (Tri-SDANet), an electroencephalogram (EEG) decoding model incorporating neuroanatomical priors derived from EEG source analysis to enhance decoding accuracy and provide interpretability in complex auditory scenes. Recognizing the inherent limitations of stand-alone BCI systems (notably their high false alarm rates), we further develop an adaptive confidence-based brain-machine fusion strategy that intelligently combines decisions from both the BCI and conventional acoustic detection models. This hybrid approach effectively merges the complementary strengths of neural perception and acoustic feature learning. We validate the proposed method through experiments with 16 participants. Experimental results demonstrate that the Tri-SDANet achieves state-of-the-art performance in neural decoding under complex acoustic conditions. Moreover, the hybrid system maintains reliable detection performance at low SNR levels while exhibiting remarkable generalization to unseen target classes. In addition, source-level EEG analysis reveals distinct brain activation patterns associated with target perception, offering neuroscientific validation for our model design. This work pioneers a neuro-acoustic fusion paradigm for robust STD, offering a generalizable solution for real-world applications through the integration of noninvasive neural signals with artificial intelligence.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0438"},"PeriodicalIF":18.1,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12531490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145330972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmenting Electroencephalogram Transformer for Steady-State Visually Evoked Potential-Based Brain-Computer Interfaces.","authors":"Jin Yue, Xiaolin Xiao, Kun Wang, Weibo Yi, Tzyy-Ping Jung, Minpeng Xu, Dong Ming","doi":"10.34133/cbsystems.0379","DOIUrl":"10.34133/cbsystems.0379","url":null,"abstract":"<p><p><b>Objective:</b> Advancing high-speed steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI) systems requires effective electroencephalogram (EEG) decoding through deep learning. However, challenges persist due to data sparsity and the unclear neural basis of most augmentation techniques. Furthermore, effective processing of dynamic EEG signals and accommodating augmented data require a more sophisticated model tailored to the unique characteristics of EEG signals. <b>Approach:</b> This study introduces background EEG mixing (BGMix), a novel data augmentation technique grounded in neural principles that enhances training samples by replacing background noise between different classes. Building on this, we propose the augment EEG Transformer (AETF), a Transformer-based model designed to capture the temporal, spatial, and frequential features of EEG signals, leveraging the advantages of Transformer architectures. <b>Main results:</b> Experimental evaluations of 2 publicly available SSVEP datasets show the efficacy of the BGMix strategy and the AETF model. The BGMix approach notably improved the average classification accuracy of 4 distinct deep learning models, with increases ranging from 11.06% to 21.39% and 4.81% to 25.17% in the respective datasets. Furthermore, the AETF model outperformed state-of-the-art baseline models, excelling with short training data lengths and achieving the highest information transfer rates (ITRs) of 205.82 ± 15.81 bits/min and 240.03 ± 14.91 bits/min on the 2 datasets. <b>Significance:</b> This study introduces a novel EEG augmentation method and a new approach to designing deep learning models informed by the neural processes of EEG. These innovations significantly improve the performance and practicality of high-speed SSVEP-based BCI systems.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0379"},"PeriodicalIF":18.1,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ergonomic Insect Headgear and Abdominal Buckle with Surface Stimulators Manufactured via Multimaterial 3D Printing: Snap-and-Secure Installation of Noninvasive Sensory Stimulators for Cyborg Insects.","authors":"Phuoc Thanh Tran-Ngoc, Kewei Song, Thu Ha Tran, Kazuki Kai, Qifeng Lin, Hirotaka Sato","doi":"10.34133/cbsystems.0406","DOIUrl":"10.34133/cbsystems.0406","url":null,"abstract":"<p><p>Insects have been integrated with electronic systems to create cyborg insects for various practical applications by utilizing their inherent adaptability and mobility. Nevertheless, most cyborg insects' preparation depends on the invasive method, which can cause harm to critical sensory organs and restrict the obstacle-negotiating capabilities of cyborg insects. We present wearable devices with headgear and abdominal buckle that address these challenges using hooking mechanisms, multimaterial 3-dimensional printing, and selective electroless plating. These devices attach securely to the antenna scape and abdominal tergum without damaging functional organs, thereby preserving the insect's natural sensory functions and physical intactness. Besides, the electrodes attach and detach easily without using adhesives, reducing the time required for cyborg insect preparation and enabling the reuse of insects. Experiments show that cyborg insects with wearable devices spend less time traversing obstacles than those prepared using invasive methods. Additionally, the potential for practical navigation tasks is further demonstrated by the cyborg insect's capacity to navigate along the \"S\"-path. This work advances scalable, efficient, and ethical utilization of cyborg insects in the fields of robotics and biohybrid systems.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0406"},"PeriodicalIF":18.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12451109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced Imaging Strategies Based on Intelligent Micro/Nanomotors.","authors":"Dang Zhang, Liang Lin, Chao Deng, Mohamed Syazwan Osman, Paul E D Soto Rodriguez, Fei Han, Mingyu Li, Lei Wang","doi":"10.34133/cbsystems.0384","DOIUrl":"10.34133/cbsystems.0384","url":null,"abstract":"<p><p>Biological imaging has revolutionized tissue analysis by revealing morphological and physiological dynamics, yet faces inherent limitations in penetration depth and resolution. Micro/nanomotors (MNMs), with autonomous propulsion and spatiotemporal control, offer transformative solutions to traditional static imaging paradigms. These dynamic contrast agents enhance detection sensitivity in ultrasound, fluorescence, photoacoustic, and magnetic resonance imaging via motion-amplified signal modulation, enabling real-time tracking of subcellular events and microenvironmental changes. While MNMs-enhanced bioimaging has advanced rapidly, systematic analysis of their mechanisms and challenges remains limited. Based on our research experience in this field, this paper first summarizes the signal-enhancing mechanisms of MNMs in single-modal imaging. It then explores multimodal applications through MNMs-probe design and discusses artificial intelligence-driven intelligent MNMs for precision imaging. Finally, challenges and outlook are outlined, aiming to provide a theoretical framework and research roadmap for MNMs-mediated bioimaging technologies.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0384"},"PeriodicalIF":18.1,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12420953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Metadata-Constrained iRadonMAP Framework with Mutual Learning for All-in-One Computed Tomography Imaging.","authors":"Hao Wang, Xiaoyu Zhang, Hengtao Guo, Xuebin Ren, Shipeng Wang, Fenglei Fan, Jianhua Ma, Dong Zeng","doi":"10.34133/cbsystems.0376","DOIUrl":"10.34133/cbsystems.0376","url":null,"abstract":"<p><p>With the increasing use of computed tomography (CT), concerns about radiation dose have grown. Deep-learning-based methods have shown great promise in improving low-dose CT image quality while further reducing patient dose. However, most deep-learning-based methods are trained on vendor-specific CT datasets with varying imaging conditions and dose levels, which results in poor generalizability across vendors due to marked data heterogeneity. Moreover, the centralization of multicenter datasets is restricted by the high costs of data collection and privacy regulations. To overcome these challenges, we propose FedM2CT, a federated metadata-constrained method with mutual learning for all-in-one CT reconstruction. This method enables simultaneous reconstruction of multivendor CT images with different imaging geometries and sampling protocols in one framework. Specifically, FedM2CT consists of 3 modules: task-specific iRadonMAP (TS-iRadonMAP), condition-prompted mutual learning (CPML), and federated metadata learning (FMDL). TS-iRadonMAP performs task-specific low-dose reconstruction, CPML shares condition-prompted knowledge between clients and the server, and FMDL aggregates model parameters with a metamodel to effectively mitigate the effect of data heterogeneity. Extensive experiments under 3 different settings demonstrate that the proposed FedM2CT achieves outstanding results compared to other methods, both qualitatively and quantitatively, showing the potential to achieve the goal of all-in-one CT reconstruction with different low-dose tasks, i.e., low-milliampere-second, sparse-view, and limited-angle.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0376"},"PeriodicalIF":18.1,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12381943/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BioCompNet: A Deep Learning Workflow Enabling Automated Body Composition Analysis toward Precision Management of Cardiometabolic Disorders.","authors":"Jianyong Wei, Hongli Chen, Lijun Yao, Xuhong Hou, Rong Zhang, Liang Shi, Jianqing Sun, Cheng Hu, Xiaoer Wei, Weiping Jia","doi":"10.34133/cbsystems.0381","DOIUrl":"10.34133/cbsystems.0381","url":null,"abstract":"<p><p>Growing evidence highlights the importance of body composition (BC), including bone, muscle, and adipose tissue (AT), as a critical biomarker for cardiometabolic risk stratification. However, conventional methods for quantifying BC components using medical images are hindered by labor-intensive workflows and limited anatomical coverage. This study developed BioCompNet-an end-to-end deep learning workflow that integrates dual-parametric magnetic resonance imaging (MRI) sequences (water/fat) with a hierarchical U-Net architecture to enable fully automated quantification of 15 biomechanically critical BC components. BioCompNet targets 10 abdominal compartments (vertebral bone, psoas muscles, core muscles, subcutaneous AT [SAT], superficial SAT, deep SAT, intraperitoneal AT, retroperitoneal AT, visceral AT, and intermuscular AT [IMAT]) and 5 thigh compartments (femur, muscle, SAT, IMAT, and vessels). The workflow was developed on 8,048 MRI slices from a community-based cohort (<i>n</i> = 503) and independently validated on 240 MRI slices from a tertiary hospital (<i>n</i> = 30). The model's performance was benchmarked against expert annotations. On internal and external validation datasets, BioCompNet achieved average Dice similarity coefficients of 0.944 and 0.938 for abdominal compartments and 0.961 and 0.936 for thigh compartments, respectively. Excellent interreader reliability was observed (intraclass correlation coefficient ≥ 0.881) across all quantified features, and IMAT quantification showed a strong linear trend (<i>P</i> _trend < 0.001) compared to physician-rated assessments. The workflow substantially reduced processing time from 128.8 ± 5.6 to 0.12 ± 0.001 min per case. By enabling rapid, accurate, and comprehensive volumetric analysis of BC components, BioCompNet establishes a scalable framework for precision cardiometabolic risk assessment and clinical decision support.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0381"},"PeriodicalIF":18.1,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12367250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated Monolithic Synaptic Device for C-Tactile Afferent Perception and Robot Emotional Interaction.","authors":"Yue Li, Lu Yang, Qianbo Yu, Yi Du, Ning Wu, Wentao Xu","doi":"10.34133/cbsystems.0367","DOIUrl":"10.34133/cbsystems.0367","url":null,"abstract":"<p><p>C-tactile afferents are low-threshold mechanoreceptors that innervate the hairy skin of mammals, essential for emotional interactions. Replication of such a mechanism could facilitate emotional interactions between humans and embodied intelligence robotic systems. Herein, we demonstrate a monolithic synaptic device that replicates and integrates tactile sensing and neuromorphic processing functions for in-sensor computing. The device is operable by both mechanical and electrical inputs, with the mechanoelectrical operation mechanism stemming from the synergistic effect of dynamic ionic migration and injection. As a proof of concept, the device effectively converts spatiotemporal tactile stimuli into distinct electrical signals, which are subsequently encoded to enable the microcomputer to classify multiple discrete emotional states, such as happiness, calmness, and excitement. This monolithic integrated device, which converges mild tactile perception with neuromorphic processing, with high tactile sensitivity and low-energy consumption, establishes an approach for emotional interaction between intelligent robots and human beings.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0367"},"PeriodicalIF":18.1,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the Gap to Bionic Motion: Challenges in Legged Robot Limb Unit Design, Modeling, and Control.","authors":"Junhui Zhang, Jinyuan Liu, Huaizhi Zong, Pengyuan Ji, Lizhou Fang, Yong Li, Huayong Yang, Bing Xu","doi":"10.34133/cbsystems.0365","DOIUrl":"10.34133/cbsystems.0365","url":null,"abstract":"<p><p>Motivated by the agility of animal and human locomotion, highly dynamic bionic legged robots have been extensively applied across various domains. Legged robotics represents a multidisciplinary field that integrates manufacturing, materials science, electronics, and biology, and other disciplines. Among its core subsystems, the lower limbs are particularly critical, necessitating the integration of structural optimization, advanced modeling techniques, and sophisticated control strategies to fully exploit robots' dynamic performance potential. This paper presents a comprehensive review of recent developments in the structural design of single-legged robots and systematically summarizes prevailing modeling approaches and control strategies. Key challenges and potential future directions are also discussed, serving as a reference for the future application of state-of-the-art manufacturing and control methodologies in legged robotic systems.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0365"},"PeriodicalIF":18.1,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-Based Control of a Continuum Manipulator with Online Jacobian Error Compensation Using Kalman Filtering.","authors":"Yujia Zhai, Jihao Xu, Hangjie Mo, Chunqi Zhang, Dong Sun","doi":"10.34133/cbsystems.0339","DOIUrl":"10.34133/cbsystems.0339","url":null,"abstract":"<p><p>Flexible continuum robots exhibit excellent adaptability to a wide range of tasks and environments. However, accurate and efficient modeling and control remain challenging due to their inherent nonlinearities. In this article, a hybrid model-based and online data-driven control method is proposed for a tendon-driven continuum robot, which requires no prior dataset collection or training. The method incorporates the Jacobian derived from the piecewise constant curvature model with online Jacobian error compensation using a Kalman filter. Consecutive Jacobian estimates are constrained to reduce fluctuations and improve stability in real-time estimation. Experimental results validate the effectiveness of the proposed hybrid approach in enhancing tracking accuracy and demonstrate its robustness against external disturbances.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":"6 ","pages":"0339"},"PeriodicalIF":18.1,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12329213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144801090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}