IEEE Transactions on Biomedical Engineering最新文献

{"title":"S5: Self-Supervised Learning Boosts Sleep Spindle Detection in Single-Channel EEG via Temporal Segmentation.","authors":"Zhen Mei, Yanshuang Liu, Mingle Sui, Alan Luiz Eckeli, Yudan Lv, Yuan Zhang, Xiaoqing Hu, Huan Yu","doi":"10.1109/TBME.2026.3675979","DOIUrl":"https://doi.org/10.1109/TBME.2026.3675979","url":null,"abstract":"<p><strong>Objective: </strong>Sleep spindles, characteristic waveforms of N2 sleep in EEG, are associated with various neural processes such as cognitive function. However, their identification relies on visual inspection by experts-a time-consuming, labor-intensive, and low inter-rater consistency process that impedes cutting edge spindle research.</p><p><strong>Methods: </strong>We introduce S5, an automatic method for sleep spindle detection employing a novel encoder-decoder architecture for time-series segmentation. A two-stage training paradigm, comprising task-agnostic pre-training followed by downstream fine tuning, ensures high-precision identification.</p><p><strong>Results: </strong>S5 demonstrates robust and competitive performance on two public datasets. On the multi-expert annotated MODA dataset, our method outperforms the average human expert. We further conducted an exploratory analysis on a large-scale unlabeled dataset of over 7,000 recordings as a physiological sanity check.</p><p><strong>Significance: </strong>S5 offers a precise and efficient solution for automating spindle detection, thereby accelerating related research. An accompanying graphical toolbox makes our method accessible for simple and intuitive analysis.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147485629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TBMSCCN: Two-Branch Multi-Scale Convolutional Correlation Network for Steady-State Visual Evoked Potential Classification.","authors":"Xinjie He, Ian Daly, Wenhao Gu, Yixin Chen, Xiao Wu, Weijie Chen, Xingyu Wang, Andrzej Cichocki, Jing Jin","doi":"10.1109/TBME.2026.3676014","DOIUrl":"https://doi.org/10.1109/TBME.2026.3676014","url":null,"abstract":"<p><p>In recent years, artificial neural networks have been effectively used to improve the target recognition performance of steady-state visual evoked potential (SSVEP) based Brain-Computer interfaces (BCIs). However, these models require the collection of a large number of calibration trials from users, which typically results in a poor user experience. When fewer calibration trials are acquired this leads to insufficient training of model parameters and weak recognition performance. To tackle these issues, this study proposes a two-branch multi-scale convolutional correlation network (TBMSCCN) in which a correlation network framework is introduced to reduce the model training parameters and prior knowledge of the SSVEP is used to enhance the model representation ability and convergence. First, a multi-scale temporal convolution module is designed to learn local temporal dependencies in a parallel two-branch feature extraction module. Next, a contrastive loss function is constructed in the latent feature space, which can guide the model to learn the intra-class consistent features while speeding up model convergence. Finally, a group convolution module is used as a decision layer to reduce the network parameters, while learning distinguishability features between targets and non-targets. Our offline tests on two public datasets show that proposed TBMSCCN method outperforms TRCA, eTRCA, DNN, Conv-CA and Bi-SiamCA in individual calibration scenarios, which can achieve an average information transform rates (ITRs) of 378.03 ± 139.18 bit/min and 198.92 ± 111.27 bit/min on the \"Benchmark\" dataset and the \"Beta\" dataset respectively. Additionally, proposed TBMSCCN method outperform FBCCA, ttCCA, EEGNet, and TST-CFSR in calibration-free scenarios. Furthermore, an online Chinese spelling experiment confirmed the real-world effectiveness of the proposed method. The proposed model has the characteristics of low parameter and strong robustness, which can facilitate the practical engineering application of SSVEP-Based-BCI system. The code is available at https://github.com/xinjieHe123/TBMSCCN.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147485624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual Graph Strategy with Diffusion Tensor Imaging for Autism Spectrum Disorder Diagnosis.","authors":"Zhixin Lin, Xiumei Liu, Mingchao Li, Minghui Deng, Lifang Wei, Riqing Chen, Ruqi Fang","doi":"10.1109/TBME.2026.3675295","DOIUrl":"https://doi.org/10.1109/TBME.2026.3675295","url":null,"abstract":"<p><strong>Objective: </strong>Diffusion Tensor Imaging (DTI) is a special magnetic resonance imaging (MRI) technique. Most of the existing research on DTI data primarily focuses either on Structural Connectivity (SC) networks derived from DTI or on DTI-derived metrics like Fractional Anisotropy, Mean Diffusivity, $lambda _{1}$, $lambda _{2}$, and $lambda _{3}$. This may lead to the neglect of potential complementary information provided by different graphs, thereby preventing the improvement of classification performance. In this study, we propose a graph neural network framework based on a dual graph strategy using DTI data for the diagnosis of ASD.</p><p><strong>Methods: </strong>Specifically, we have done the following: 1) To address the challenges of small datasets and class imbalance, we employed data augmentation techniques (including replication of minority class samples and the mixup method) to enhance data diversity and representativeness. 2) We combined a threshold-based real physical connectivity adjacency matrix with a local microstructure adjacency matrix learned from node features to mitigate the limitations of relying on single structural information. 3) We designed a Multi-Layer Pooling Fusion (MLPF) method to capture multi-layered and richer feature representations.</p><p><strong>Results: </strong>Our proposed method was evaluated on 198 subjects and the experimental results showed that our proposed method outperformed multiple existing methods in five-fold cross-validation, achieving 75.24% accuracy and 73.12% AUC.</p><p><strong>Conclusion: </strong>DTI is crucial for analyzing connectivity abnormalities in ASD. Our proposed method enables more efficient, objective, and reliable diagnosis of ASD.</p><p><strong>Significance: </strong>This work provides a valuable reference framework for utilizing DTI data in research on neurological disorders.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147480624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Disorders of Consciousness Using Temporal Sleep Dynamics Extracted From Whole-Night PSG.","authors":"Jun Xiao, Tianyou Yu, Haiyun Huang, Jiahui Pan, Fei Wang, Di Chen, Zhenghui Gu, Zhuliang Yu, Benyan Luo, Yuanqing Li","doi":"10.1109/TBME.2026.3675367","DOIUrl":"https://doi.org/10.1109/TBME.2026.3675367","url":null,"abstract":"<p><p>Accurate assessment of patients with disorders of consciousness (DoC) remains a major clinical challenge due to the limitations of behavior-based evaluations and task-dependent neurophysiological paradigms. Whole-night polysomnography (PSG), a passive and noninvasive monitoring tool, offers unique potential for revealing residual brain function during sleep. In this study, we propose a temporal-dynamic feature extraction and aggregation framework for PSG analysis to enable machine learning-based diagnosis and prognosis in DoC patients. Whole-night EEG/EOG signals were segmented into non-overlapping 30-second epochs, from which time-domain, spectral, and nonlinear complexity features were extracted. To obtain a unified and compact representation of variable-length feature sequences, two aggregation strategies were applied: stage- wise averaging based on sleep staging and clustering-based grouping via unsupervised learning. A two-stage feature selection pipeline further reduced dimensionality while preserving discriminative power and interpretability. Classifiers trained on the aggregated features achieved strong performance in distinguishing minimally conscious state (MCS) from vegetative state (VS), with AUC values exceeding 0.84, and demonstrated robust predictive ability for long-term recovery outcomes (AUC=0.79). These findings highlight the diagnostic and prognostic value of whole-night PSG and support the development of fully automated, task-free assessment tools for DoC.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147480620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Steering the Path with a Semi-Passive Robot to Break Post-Stroke Synergies.","authors":"Thomas E Augenstein, C David Remy, Shreeya Buddaraju, Edward S Claflin, Chandramouli Krishnan","doi":"10.1109/TBME.2026.3674710","DOIUrl":"https://doi.org/10.1109/TBME.2026.3674710","url":null,"abstract":"<p><strong>Objective: </strong>Abnormal coupling of elbow flexors with shoulder abductors-the \"flexor synergy\"-is a common post stroke motor impairment that interferes with upper extremity function. Previous studies have shown that practicing elbow extension while loading shoulder abductors can improve independent joint control. However, these approaches often involve expensive and bulky equipment, limiting their use in clinic. SepaRRo is a semi-passive rehabilitation robot that uses brakes to generate training forces, reducing its cost relative to existing systems. SepaRRo can also load the shoulder abductors during horizontal planar reaching, suggesting that it could target the flexor synergy. However, it is unclear if training with a semi-passive robot can produce out-of-flexor synergy kinematic adaptations.</p><p><strong>Methods: </strong>Chronic stroke survivors (n = 15) with upper extremity impairment participated in a randomized, crossover-design experiment where they reached for a functional target with their more-impaired limb in two conditions: SepaRRo resisting their motion to the target (Resistance), and SepaRRo generating an additional lateromedial force to load their shoulder abductors (Steering). For each condition, we measured changes in reaching kinematics with motion capture equipment.</p><p><strong>Results: </strong>Following the Steering condition, participants demonstrated significantly greater shoulder abduction than the Pre-test and the Resistance condition (p0.112). Participants reduced their el bow extension following the Resistance condition (p = 0.018).</p><p><strong>Conclusion: </strong>Steering facilitated out-of-synergy adaptations that were not present following simple resistance.</p><p><strong>Significance: </strong>Conventional training methods may facilitate post-stroke synergies and impede recovery, while SepaRRo's steering forces may lead to improvements in independent joint control in stroke survivors.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147467841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Zero-Shot Deep Anti-Aliasing Prior for Residual Artifact Suppression in non-Cartesian k-space MRI.","authors":"Chuanjiang Cui, Jaeuk Yi, Soo-Hyung Lee, Changmin Ryu, Dong-Wook Kim, Chan-Hee Park, Kyu-Jin Jung, Dong-Hyun Kim","doi":"10.1109/TBME.2026.3674149","DOIUrl":"https://doi.org/10.1109/TBME.2026.3674149","url":null,"abstract":"<p><p>Non-Cartesian k-space sampling in MRI is widely used, yet images reconstructed on scanners with preliminary corrections (e.g. off-resonance) often exhibit residual artifacts (e.g. ringing and streaking) that can compromise interpretation. We propose a zero-shot residual artifact suppression method that operates directly on scanner-reconstructed images without requiring labeled data, pre-training, or an explicit degradation model. The method builds on a decoder-style generative prior and incorporates a fixed blur-kernel operator that reshapes the network's inductive bias without introducing additional learnable parameters. We formulate the procedure as an optimization problem by minimizing a data-fidelity objective between the network output and the corrupted input image. We evaluate the method on simulated data and demonstrate improved image quality over conventional baselines, while remaining competitive with supervised comparisons under acceleration factors up to R = 4. Across these settings, relative to the artifact-corrupted input, SSIM improves by up to 38% and PSNR increases by up to 10.64 dB. In in vivo experiments, the proposed method consistently attenuates residual aliasing-like artifacts, indicating reproducible performance across acquisitions. Overall, the proposed framework offers a practical and general-purpose post-processing strategy for artifact suppression in non-Cartesian MRI, with applicability across diverse sampling patterns and imaging settings.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147456886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Electric Current Field Source Reconstruction Method for Coordinate Positioning of Pulmonary Interventional Surgical Actuator Terminal.","authors":"Wei Zhang, Jingang Wang, Pengcheng Zhao, Wei He, Qi Jiang, Hekai Yang, Haiting Xia, Xiaotian Wang","doi":"10.1109/TBME.2026.3673959","DOIUrl":"https://doi.org/10.1109/TBME.2026.3673959","url":null,"abstract":"<p><p>The advancement of intelligent surgery has imposed greater requirements on the precision and real-time performance of pulmonary minimally invasive surgical navigation. However, existing intraoperative navigation techniques, including optical tracking, X-ray imaging, and magnetic resonance imaging (MRI), have inherent limitations such as inadequate real-time performance, complicated workflows, strong equipment dependency, and restricted visual fields. These constraints hinder the ability of interventional surgeries to provide continuous and stable three-dimensional coordinate feedback in deep, non-line-of-sight environments. Therefore, this study proposes an electric current field source reconstruction method for determining the terminal coordinates of surgical actuators. An electric current is injected from the tip of the surgical instrument, creating an electric field within the human tissue. The potential measured by surface electrodes are then used to reconstruct the current source coordinates, enabling real-time and active sensing of the surgical probe coordinates. A mathematical model for electric current field-based coordinate positioning was developed, involving analyses of the forward and inverse problems as well as coordinate reconstruction. Random single-point positioning simulations were conducted, and a 16 + 1-electrodes experimental platform was constructed for coordinates navigation tests to evaluate positioning and navigation performance. In addition, dynamic positioning experiments of multiple physiological tissues were carried out to assess the robustness and anti-interference capability of the proposed method. Experimental results indicate that the positioning error remains within 2 mm under single-point, linear, and curved trajectory conditions, satisfying the precision requirements for intraoperative navigation. This method significantly improves the accuracy and safety of surgical positioning and navigation, thereby holding substantial engineering significance and clinical value for the advancement of intelligent surgical systems.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147456845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hypothesis on the Mechanism of Normal Pressure Hydrocephalus Involving Brain Fluid Interactions: A Mathematical Approach.","authors":"Galina S Valova, Olga B Bogomyakova, Andrey A Tulupov, Alexander A Cherevko","doi":"10.1109/TBME.2026.3673860","DOIUrl":"https://doi.org/10.1109/TBME.2026.3673860","url":null,"abstract":"<p><strong>Objective: </strong>Hydrocephalus is a severe disorder characterized by pathological enlargement of the brain ventricles, leading to compression and deformation of brain tissue. The pathophysiological mechanisms underlying some subtypes of hydrocephalus remain poorly understood. Normal pressure hydrocephalus (NPH) continues to be a clinically significant and unresolved issue in elderly care. This study proposes a novel approach to investigate this pathology using mathematical modeling techniques.</p><p><strong>Methods: </strong>Using stationary multicomponent poroelasticity equations with physiological boundary conditions, we examine the interactions between brain parenchyma and fluid (including arterial, capillary, venous blood, and interstitial fluid). The model describes these interactions through four specific\"interaction coefficients\".</p><p><strong>Results: </strong>Analysis revealed how interaction coefficients govern ventricular wall pressure and displacement. The derived analytical approximations of these relationships provide a foundation for hypothesizing the mechanisms of NPH initiation and development.</p><p><strong>Conclusions: </strong>This hypothesis suggests that NPH results from compromised vascular autoregulation, which under normal conditions maintains stable ventricular volume.</p><p><strong>Significance: </strong>This work identifies specific interaction parameters that govern transitions between physiological stability and pathological ventricular dilation. These results may assist in refining diagnostic criteria and in developing therapeutic strategies aimed at correcting the condition and treating NPH.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147456877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a Machine Learning-Driven Digital Twin for Real-Time Hormone Biosensing in Personalized Infertility Care.","authors":"Anastasiia Gorelova, Alexandra Parichenko, Shirong Huang, Santiago Melia, Gianaurelio Cuniberti","doi":"10.1109/TBME.2026.3674340","DOIUrl":"https://doi.org/10.1109/TBME.2026.3674340","url":null,"abstract":"<p><strong>Background: </strong>The increasing demand for personalized healthcare solutions highlights the limitations of one-size-fits-all treatment strategies. Digital twin (DT) technology, which enables real-time virtual replicas of physical systems, offers a promising approach to advance personalized medicine through continuous monitoring, simulation, and prediction.</p><p><strong>Methods: </strong>This study presents the foundational phase of a machine-learning-driven biosensor DT designed to support personalized infertility treatment through integration with a Smart Health Monitoring System. The DT replicates the behavior of a field-effect transistor (FET)-based biosensor functionalized with 17$beta$-estradiol aptamers and trained on experimental data obtained from silicon nanonet BioFET prototypes.</p><p><strong>Results: </strong>Seven supervised machine learning algorithms were evaluated to predict hormone concentration from electrical parameters ($V_{g}$, $I_{sd}$). The K-Nearest Neighbors (KNN) model achieved the highest predictive accuracy ($R^{2} = 0.99$, $text{CV}text{-}R^{2} = 0.98$, RMSE = 11.87 pg/mL) and demonstrated robust cross-device generalization under Leave-One-Biosensor-Out validation ($R^{2} = 0.59$). These results confirm the model's capability to capture nonlinear relationships and generalize across independently fabricated sensors.</p><p><strong>Conclusion: </strong>The developed model constitutes a validated predictive core of a biosensor digital twin. At the current stage, the DT is limited to predictive modeling and does not yet implement real-time synchronization or closed-loop feedback, which are planned in future work.</p><p><strong>Significance: </strong>This study establishes a practical framework for data-driven digital twins of biosensors and demonstrates their potential for integration into smart health monitoring systems supporting personalized infertility care. The proposed approach provides a foundation for real-time, adaptive, and clinically relevant biosensor twins in precision medicine.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147456851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Low-complexity Programmable Ultrasound Stimulation System: Design and Safety Evaluation.","authors":"Xuanjie Ye, Meimei Guo, Tianyi Wang, Sujie Wang, Jingjia Yuan, Tao Tan, Wenhua Shen, Li Huang, Fo Hu, Yu Sun","doi":"10.1109/TBME.2026.3673152","DOIUrl":"https://doi.org/10.1109/TBME.2026.3673152","url":null,"abstract":"<p><p>Accurate control, monitoring of acoustic power, and flexible waveform generation are essential for safe and reproducible transcranial focused ultrasound (tFUS) neuromodulation, which is not comprehensively supported by existing benchtop platforms. This work presents a low-complexity and programmable tFUS stimulation system. The system integrates a direct digital synthesis module, a DAC-controlled programmable DC-DC supply, a full-bridge driver, and an impedance matching network to achieve flexible waveform generation and efficient transducer excitation. Acoustic power is monitored using a nonuniform discrete Fourier transform method at the driving frequency. Direct amplitude regulation enables highly linear pressure control up to 4.85 MPa. Impedance matching raised the maximum peak-to-peak excitation voltage from 86 V to 206 V (×2.4) and reduced total harmonic distortion (THD) by 19.19 dB. The power monitor achieves <5% error for outputs above 3 W. In vivo safety was evaluated in mice using both acute (single 20-min exposure) and chronic (21-day, 20-min/day) protocols. Four stimulation groups at $mathrm{I_{SPPA}}$ = 40 W/cm² with duty cycles from 1.8% to 14.4% ($mathrm{I_{SPTA}}$ = 0.72-5.76 W/cm²) were compared with sham and controls. Behavioral outcomes and histological analysis revealed no abnormalities under these conditions. The $mathrm{I_{SPTA}}$ range corresponds to one to eight times the FDA guideline limit, thereby encompassing and extending typical safety margins in neuromodulation studies. These results demonstrate the feasibility of the proposed platform, with validation at both the circuit level and through preclinical safety studies.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}