IEEE Transactions on Biomedical Engineering最新文献

{"title":"Geometry-Dependent Properties of Physically Crosslinked PPy-PVA Hydrogels Enable Tunable Iontophoretic Drug Delivery.","authors":"Heather Jin Hee Kim, Mohsen Alishahi, Kyuin Park, Mikaila K Roncevich, Larissa M Shepherd, Cara M Nunez","doi":"10.1109/TBME.2026.3744496","DOIUrl":"https://doi.org/10.1109/TBME.2026.3744496","url":null,"abstract":"<p><strong>Objective: </strong>Iontophoresis enables noninvasive transdermal drug delivery. The control of drug release, however, has largely relied on current modulation. Here, we explore geometry-driven factors, gel microstructure, and local electric fields, as alternative levers for regulating drug transport.</p><p><strong>Methods: </strong>To investigate iontophoretic drug delivery, we encapsulate bovine serum albumin (BSA) in polypyrrole-polyvinyl alcohol (PPy-PVA) hydrogels. Five distinct hydrogel geometries were fabricated to impose variation in pore architecture and electric field distribution, including Hilbert curve-based designs that use increased corner density to enhance local electric field strength. Using formulation studies, we determined the ideal PPy content, drug loading efficiency, and current density. Maximum field strength of individual hydrogel geometries was simulated via finite element analysis, and microstructures were characterized by scanning electron microscopy. Lastly, we assessed differences in the transport metrics of the hydrogel geometries through an in vitro study using a Franz diffusion cell and an artificial membrane.</p><p><strong>Results: </strong>The engineered geometries produced clearly differentiated microstructures and electric field strengths. Drug delivery studies demonstrated that iontophoresis substantially enhanced permeability compared with passive diffusion, with each geometry yielding different profiles in cumulative release, flux, and permeability. Notably, the average iontophoretic permeability was 4.32 × 10^-5 cm s^-2, exceeding values reported in previous studies using artificial membranes.</p><p><strong>Conclusion: </strong>This study showed that the geometry-driven fabrication of drug-loaded hydrogel can achieve different delivery characteristics in both iontophoretic and passive transport using artificial membranes.</p><p><strong>Significance: </strong>Geometry-mediated control complements existing approaches for iontophoretic drug delivery without requiring individual current-density control.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223923","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":"LDiT3D: Head and Neck Tumor Segmentation Using a 3D Latent Diffusion Transformer Model.","authors":"Yafei Dong, Thibault Marin, Kira Grogg, Emilie Lissavalid, Maad Galal, Arnaud Beddok, Xiaofeng Liu, Maryam Moteabbed, Jonghye Woo, Chao Ma, Georges El Fakhri","doi":"10.1109/TBME.2026.3744482","DOIUrl":"https://doi.org/10.1109/TBME.2026.3744482","url":null,"abstract":"<p><strong>Objective: </strong>Head and neck (H&N) tumors are among the most common cancers worldwide. However, manual annotation of H&N tumors for each patient poses a significant burden on clinicians. Therefore, developing automated and accurate methods for H&N tumor segmentation is of great clinical importance.</p><p><strong>Methods: </strong>We introduce LDiT3D, a novel 3D transformer-based diffusion model designed to segment H&N tumors from 3D Fluorodeoxyglucose positron emission tomography (FDG-PET) and X-ray computed tomography (CT) scans, primarily focusing on oropharyngeal tumors. LDiT3D utilizes stacked 3D vision transformer blocks to effectively capture spatial context in volumetric data. To address the high computational cost typically associated with 3D transformers, we incorporate a pre-trained autoencoder that projects the input into a lower-dimensional latent space where the diffusion process is performed more efficiently. In addition to the diffusion loss computed in latent space, a task-specific segmentation loss is introduced in pixel space to further enhance segmentation accuracy.</p><p><strong>Results: </strong>Methods based on U-Net and transformer architectures were selected as reference methods. Comparative results demonstrated that the proposed LDiT3D achieved highly accurate segmentation performance, with the highest Dice Score and lowest Hausdorff distance on both public and local datasets. Moreover, LDiT3D showed a more balanced performance between Precision and Recall, as indicated by the highest Fowlkes-Mallows Score. Ablation studies further confirmed the effectiveness of the key components in the LDiT3D design.</p><p><strong>Conclusion: </strong>LDiT3D delivers more accurate and balanced H&N tumor segmentation compared to reference methods.</p><p><strong>Significance: </strong>These findings highlight LDiT3D's potential to improve automated H&N tumor annotation and advance medical image analysis.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223896","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 Reconfigurable CMOS Microelectrode Array Featuring 24,576 Electrodes for Chemical and Electrical Neural Recording.","authors":"Jingyi Xu, Yingfei Wang, Chao Zhao, Xiaohong Li, Yun Liu, Hong Liu","doi":"10.1109/TBME.2026.3743860","DOIUrl":"https://doi.org/10.1109/TBME.2026.3743860","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to develop a high throughput, high-density, and reconfigurable CMOS microelectrode array (MEA) chip for chemical and electrical recording of biological neural networks.</p><p><strong>Methods: </strong>The continuous time/discrete-time current sensing architecture is integrated on a single chip to support electrochemical and electrophysiological measurements within the same system. The readout achieves 24,576 electrodes, an input-referred noise density of 2 fA/√Hz, a bandwidth of 5 kHz/70 kHz, and 24/384 simultaneous output channels for chemical/electrical recording, respectively. For electrochemical measurements, inter-electrode chemical crosstalk is suppressed to improve the spatial resolution of array-based electrochemical measurements.</p><p><strong>Results: </strong>The proposed chip demonstrates electrochemical sensing at individual electrodes, quantitative detection of dopamine in prepared solution, and dynamic electrochemical imaging with high spatial and temporal resolution. For electrophysiological measurements, spontaneous extracellular biocurrent is successfully recorded from primary neurons confirming the capability of the system for cell-level electrophysiological current sensing.</p><p><strong>Conclusion: </strong>Experimental results verify the applicability of the proposed MEA in recording electrochemical and electrophysiological signals from various biological samples, highlighting its potential as a versatile bioelectronic platform for multimodal neural interface applications.</p><p><strong>Significance: </strong>Compared with other MEAs, the proposed chip supports both neurotransmitter-related chemical sensing and neural current recording on the same hardware platform. Therefore, it provides a promising tool for studying the interaction between chemical and electrical signaling in biological neural networks.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149193261","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":"Artificial Intelligence in Echocardiography: A Critical Review of Models, Validation, and Clinical Impact.","authors":"Shunkai Xiao, Xianqiang Yang, Jing Chi","doi":"10.1109/TBME.2026.3743686","DOIUrl":"10.1109/TBME.2026.3743686","url":null,"abstract":"<p><strong>Objective: </strong>To critically review artificial intelligence (AI) in echocardiography, linking model design and technical performance to validation and clinical use.</p><p><strong>Methods: </strong>We synthesized literature assembled through Web of Science and Scopus collections and targeted revision searches. Evidence was organized by echocardiographic task, model paradigm, data source, evaluation protocol, and level of clinical validation.</p><p><strong>Results: </strong>AI applications span acquisition guidance, image analysis, quantitative measurement, disease assessment, and outcome prediction, with emerging foundation, multimodal, and examination-level models. Most studies assess retrospective task performance; fewer evaluate cross-site transportability or clinical impact. Reproducible performance has been demonstrated for view recognition, segmentation, and selected measurements under aligned protocols, and prospective trials confirm workflow gains. However, evidence linking AI to improved clinical decisions or patient outcomes remains limited. Generalizability is further constrained by uneven dataset coverage and inconsistent evaluation protocols.</p><p><strong>Conclusion: </strong>Current evidence supports AI assistance in selected echocardiographic tasks; however, clinical use requires external validation, failure reporting, and ongoing monitoring in intended settings.</p><p><strong>Significance: </strong>This review links model development with validation evidence to guide clinical evaluation and identify priorities for prospective studies in echocardiography.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149181562","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":"Deep Residual Learning for Iodine Estimation in Digital Breast Tomosynthesis.","authors":"Martina Nassi, Leonardo Coito, Koen Michielsen, Ioannis Sechopoulos","doi":"10.1109/TBME.2026.3737827","DOIUrl":"10.1109/TBME.2026.3737827","url":null,"abstract":"<p><strong>Objective: </strong>Dual-energy contrast-enhanced digital breast tomosynthesis (DECE-DBT) could provide the quantification of iodine contrast with a pseudo-three-dimensional lesion reconstruction, outperforming CE two-dimensional mammography, and hence be a cost-effective alternative to DCE magnetic resonance for breast cancer imaging. However, limited-angle artifacts in DBT reduce its quantitative accuracy. In this work, we propose a deep learning (DL)-based method for DECE-DBT that performs artifact-robust material decomposition for iodine concentration estimation.</p><p><strong>Methods: </strong>Evaluation was performed over 869 digital compressed breast phantoms containing heterogeneous lesions with varying iodine concentrations. The DL network consists of residual blocks, each containing a three-layer CNN followed by a U-Net, and predicts voxel-wise fractions of adipose-blood, fibroglandular-blood and iodine-blood mixtures.</p><p><strong>Results: </strong>Optimization of the number of residual blocks showed that the 2-block model was optimal for the available dataset, generating hallucinations in under 2% of the tested cases. The network performance strongly depends on lesion size: the average dice similarity coefficient was 0.82 for lesions larger than 0.7 cm in diameter. Therefore, early-stage breast tumors (≤ 2 cm) fall mostly within the size range where the network performs reliably.</p><p><strong>Conclusion: </strong>Aimed to shorten the path in making DECE-DBT a functional modality, these findings demonstrate the early potential of our DL-based approach for accurate quantification of iodine concentrations.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149282","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":"Dynamics of P wave Morphological Variability Preceding the Onset of Atrial Fibrillation.","authors":"Maikel Noriega, Alba Martin-Yebra, Martin Stridh, Pyotr G Platonov, Aleksei Savelev, Javier Marta, Juan Pablo Martinez, Leif Sornmo, Pablo Laguna","doi":"10.1109/TBME.2026.3742161","DOIUrl":"10.1109/TBME.2026.3742161","url":null,"abstract":"<p><p>Although the problem of predicting atrial fibrillation (AF) has been extensively studied, the electrophysiological changes that precede AF onset remain insufficiently understood. From a clinical perspective, better understanding of such changes should facilitate future monitoring, prediction strategies, and therapeutic decisions.</p><p><strong>Objective: </strong>To investigate the temporal characteristics of P wave morphological variability (PMV) in a 60-min segment preceding the onset of atrial fibrillation (AF).</p><p><strong>Methods: </strong>Ambulatory ECG recordings from 32 patients comprising 176 AF episodes are analyzed. The P wave is emphasized by a linear, spatial transformation of the ECG. Using a novel, robust parameter, the PMV is computed in successive 2-min subsegments of a 60-min pre-AF segment. A piecewise linear model, accounting for basic PMV characteristics, is fitted to the resulting data points, followed by clustering of the model parameter estimates together with the supraventricular ectopic burden.</p><p><strong>Results: </strong>In 63% of the segments, an increase in PMV was found with a change point ranging from 4 to 17 min before AF onset. Clustering demonstrated heterogeneous characteristics manifested by two clusters associated with an increase in PMV, and two other clusters associated with a low and stable PMV of which one had a much larger ectopic burden.</p><p><strong>Conclusions: </strong>Clustering suggests that an AF episode may be preceded by focal ectopic activity, conduction destabilization, or without any observed manifestation.</p><p><strong>Significance: </strong>The proposed framework provides a physiologically interpretable stratification of the pre-AF dynamics of the P wave morphology.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149147648","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":"Fetal Electrocardiogram Extraction Based on Tensor Decomposition of Multi-Dimensional Subspace Representation.","authors":"Lu Wang, Bo Wang, Chengqing Si, Tomoaki Ohtsuki, P Takis Mathiopoulos, Fumiyuki Adachi","doi":"10.1109/TBME.2026.3736830","DOIUrl":"10.1109/TBME.2026.3736830","url":null,"abstract":"<p><p>Since fetal ECG (FECG) produces much lower signal amplitudes than maternal ECG (MECG), it is highly susceptible to volume-conduction effects, respiration, and fetal movements. Therefore, the observed abdominal signals exhibit pronounced nonlinear mixing and time-varying characteristics, which severely limit extraction accuracy. To tackle this complex maternal-fetal mixing, this paper pro poses a tensor decomposition of multi-dimensional sub space representation (TD-MDSC) scheme for improving ex traction accuracy and robustness. First, an adaptive multi subspace decoupling (AMSD) mechanism is developed. It employs multi-subspace mapping and hierarchical decomposition to transform the nonlinear mixtures into an approximately linear and more separable representation, thereby facilitating subsequent FECG extraction. Furthermore, a multi-domain tensorized decomposition (MDTD) strategy is developed based on tensor-decomposition theory in multidimensional signal processing. It constructs a third order covariance tensor from the second-order statistics of the observed data and their time-lagged versions to capture multidimensional statistical properties and global spatiotemporal correlations in the observed mixtures. This representation enables effective modeling and separation of the complex maternal-fetal mixtures. Experiments on four public datasets show that the proposed scheme achieves competitive performance compared with several mainstream methods in terms of fetal R-peak detection, indicating robustness under nonlinear and nonstationary mixing conditions.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148459","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":"Cross-Subject Localization of Epileptogenic Zone for Drug-Resistant Epilepsy Based on the Causal Brain Network.","authors":"Yalin Wang, Weixin Kong, Yaqing Liu, Zhe Shi, Qiang Li, Tiancheng Wang, Wei Chen, Bin Hu","doi":"10.1109/TBME.2026.3741120","DOIUrl":"https://doi.org/10.1109/TBME.2026.3741120","url":null,"abstract":"<p><strong>Objective: </strong>While neurosurgical treatment is the primary intervention for Drug-resistant epilepsy (DRE) patients, its success rate is limited to approximately 60%. Precise localization of epileptogenic zone (EZ) is a critical factor for surgical success. This study aims to achieve cross-subject localization of the EZ in DRE patients using causal brain network features extracted from stereotactic electroencephalography (SEEG).</p><p><strong>Methods: </strong>We collected SEEG recordings from 12 DRE patients who achieved complete seizure freedom (Engel Class I). The causal coupling algorithm, full convergent cross mapping (FCCM), was applied to both ictal and interictal states to extract causal brain network features from SEEG recordings. We fed these features into multiple machine learning models to achieve automated identification of EZ within a leave-one-patient-out cross-validation framework. Under a unified evaluation protocol, FCCM was compared with directed information, frequency-domain convergent cross mapping (FD CCM),high-frequencyoscillation-based methods, source-sink con nectivity, and SEEGformer.</p><p><strong>Results: </strong>At the individual level, the Mann-Whitney U test demonstrated that the raw FCCM values sig nificantly differentiated the EZ from the non-epileptogenic zones (NEZ) in 83.3% (10/12) of patients during the ictal state and in all patients (12/12) during the interictal state (p < 0.001). Per mutational multivariate analysis of variance on the 8 dimensional statistical features revealed significant global network variations in 83.3% (10/12) of ictal and 91.7% (11/12) of interictal states (p < 0.001). The combined model, which integrated both ictal and interictal features using the logistic regression classifier, achieved the best performance among all evaluated methods This approach achieved the highest accuracy (0.83 ± 0.10), recall (0.87 ± 0.09), and F1-score (0.80 ± 0.12), while the second-best baseline (FD CCM) yielded an accuracy of 0.79 ± 0.15, a recall of 0.81 ± 0.20, and an F1-score of 0.75±0.18. These results demonstrate that our method achieves state-of-the-art performance.</p><p><strong>Conclusion: </strong>Causal brain network features extracted by FCCM are robust biomarkers for localizing the EZ.</p><p><strong>Significance: </strong>This study could support the development of more personalized treatment strategies for DRE patients. It is expected to promote the development of precision medicine for DRE.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149136436","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 Adaptive Grouped Multivariate Variational Mode Decomposition for Physiological Signals.","authors":"Tian Zhang, Duanpo Wu, Lian Zhang, Yuhan Gao, Tiejia Jiang, Yixuan Yuan, Xiaolong Ye, Siwei Ye, Chenggang Yan","doi":"10.1109/TBME.2026.3741411","DOIUrl":"https://doi.org/10.1109/TBME.2026.3741411","url":null,"abstract":"<p><strong>Objective: </strong>Grouped multivariate variational mode decomposition (GMVMD) is sensitive to the preset group number $G$ and common mode number $K$. To improve its adaptability to multichannel electroencephalogram (EEG) and electrocardiogram (ECG) signals, we determine both automatically.</p><p><strong>Methods: </strong>We propose dual adaptive grouped multivariate variational mode decomposition (DA-GMVMD) with three stages: adaptive grouping, adaptive $K$ search and grouped decomposition. Adaptive grouping constructs a channel graph and determines the channel groups using the Laplacian eigengap and spectral clustering. Adaptive $K$ search uses probe-mode energy shares to select a mode number for each group. Grouped decomposition applies multivariate variational mode decomposition (MVMD) to each group and combines the results.</p><p><strong>Results: </strong>On synthetic signals, DA-GMVMD recovers the underlying grouping structure and remains stable across noise levels. In a separate synthetic test, it recovers the group-specific mode numbers. On the CHB-MIT, MODMA, and PTB-XL datasets, it achieves lower reconstruction error than GMVMD with fixed parameters and several baselines related to MVMD. The reduction relative to GMVMD is significant on CHB-MIT and MODMA. On PTB-XL, DA-GMVMD has a lower observed mean reconstruction error. The reconstructed signals retain seizure classification information on CHB-MIT, regional band power and bilateral asymmetry on MODMA, and R-peak timing and ECG intervals on PTB-XL.</p><p><strong>Conclusion: </strong>DA-GMVMD adapts the channel groups and their mode numbers to the signal content while retaining the evaluated EEG and ECG information in the reconstructed signals.</p><p><strong>Significance: </strong>DA-GMVMD makes multichannel EEG and ECG decomposition less sensitive to a manually preset group number and common mode number.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149136445","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":"Design and Fabrication of an Anatomically Realistic Multimodal EEG/MEG Head Phantom.","authors":"Kevin E Alexander, Justin R Estepp","doi":"10.1109/TBME.2026.3740351","DOIUrl":"https://doi.org/10.1109/TBME.2026.3740351","url":null,"abstract":"<p><strong>Background: </strong>Accurate electromagnetic head phantoms are important tools for validating electroencephalography (EEG) and magnetoencephalography (MEG) devices and techniques. A combined M/EEG phantom would be particularly useful for its ability to test and validate multimodal imaging methods. However, EEG and combined M/EEG phantoms remain commercially unavailable, and phantoms in published literature require materials research and development processes that are difficult to complete or replicate. Here, we detail steps to create a highly realistic four-layer, desktop M/EEG phantom and demonstrate simultaneous EEG and MEG recordings of signals generated from within the phantom.</p><p><strong>Methods: </strong>Utilizing materials and methods accessible to biomedical professionals, we provide detailed instructions to create, maintain, and use an M/EEG phantom with four layers including the scalp, compact bone, spongy bone, and brain layers. We also demonstrate its use by stimulating the phantom with dipole sources at four cortical locations using current-controlled signals of varying frequencies and amplitudes.</p><p><strong>Results: </strong>The resulting phantom is anatomically and electrically realistic, with skin and brain layer resistivities of 2.668 Ω·m each, and average bulk skull resistivity of 169.2 Ω·m. The dipole stimulations also resulted in biologically realistic scalp maps and measured signal amplitudes in both the EEG and MEG.</p><p><strong>Conclusion: </strong>The instructions presented here produce a combined M/EEG phantom that exhibits biologically realistic forward physics using materials and methods available to a biomedical professional.</p><p><strong>Significance: </strong>With these instructions, we make electromagnetic head phantoms more accessible to researchers, improving their ability to test and validate EEG, MEG, and combined devices and techniques.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108436","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}