{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:43:08Z","timestamp":1792791788573,"version":"4.5.0"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171152"],"award-info":[{"award-number":["62171152"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62171152"],"award-info":[{"award-number":["62171152"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hybrid brain\u2013computer interfaces (BCIs) have attracted growing research attention because they combine the millisecond-level temporal resolution of electroencephalography (EEG) with the spatially informative hemodynamic responses of functional near-infrared spectroscopy (fNIRS). However, most existing deep fusion methods rely on static late-fusion strategies, which tend to underexploit latent cross-modal dependencies and are vulnerable to modality-specific signal degradation. To address these limitations, we propose MGFNet, a multi-granularity fusion network for hybrid BCI decoding. MGFNet contains three components: (1) intra-modal encoders that learn modality-specific spatiotemporal representations from EEG, oxygenated hemoglobin (HbO), and deoxygenated hemoglobin (HbR) signals; (2) cross-modal interaction encoders that temporally align paired modalities and use dilated convolutions to capture long-range EEG-fNIRS dependencies; and (3) a Coupling-Guided Sparse Component Routing (CGSCR) module that estimates sample-specific cross-modal coupling and performs adaptive discrete routing. We further introduce a deep supervision strategy to stabilize optimization and improve branch-level discriminability. Under a within-subject held-out evaluation protocol on a public benchmark dataset, MGFNet achieved classification accuracies of 99.40% on the n-back task and 99.03% on the word generation (WG) task, outperforming representative comparison methods evaluated under a matched protocol. Ablation studies further confirmed the contributions of the intra-modal encoders, the cross-modal interaction encoders, and the CGSCR module. Under controlled EEG corruption with additive white Gaussian noise at \u221210 dB, MGFNet outperformed a static-fusion variant by 9.23 percentage points on the n-back task and 6.31 percentage points on the WG task. These results support the effectiveness of MGFNet in the present offline within-subject setting and indicate improved robustness under controlled single-modality degradation.<\/jats:p>","DOI":"10.3390\/s26113402","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T14:36:56Z","timestamp":1779892616000},"page":"3402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7644-7480","authenticated-orcid":false,"given":"Yan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2702-6389","authenticated-orcid":false,"given":"Xiaoyu","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2936-146X","authenticated-orcid":false,"given":"Xiaoyang","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mridha, M.F., Das, S.C., Kabir, M.M., Lima, A.A., Islam, M.R., and Watanobe, Y. (2021). Brain-Computer Interface: Advancement and Challenges. Sensors, 21.","DOI":"10.3390\/s21175746"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, J., Xia, Y., Zhou, X., Vidal Rosas, E., Thomas, A., Loureiro, R., Cooper, R.J., Carlson, T., and Zhao, H. (2023). fNIRS-EEG BCIs for Motor Rehabilitation: A Review. Bioengineering, 10.","DOI":"10.3390\/bioengineering10121393"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bourguignon, N.J., Lo Bue, S., Guerrero-Mosquera, C., and Borrag\u00e1n, G. (2022). Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research. Front. Neuroergon., 3.","DOI":"10.3389\/fnrgo.2022.934234"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ahn, S., and Jun, S.C. (2017). Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces-Current Limitations and Future Directions. Front. Hum. Neurosci., 11.","DOI":"10.3389\/fnhum.2017.00503"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102595","DOI":"10.1016\/j.bspc.2021.102595","article-title":"A systematic review on hybrid EEG\/fNIRS in brain-computer interface","volume":"68","author":"Liu","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, R., Yang, D., Fang, F., Hong, K.-S., Reiss, A.L., and Zhang, Y. (2022). Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review. Sensors, 22.","DOI":"10.3390\/s22155865"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neuroscience.2025.12.036","article-title":"Classification of EEG-fNIRS bimodal brain signals for motor imagery tasks based on wavelet transform and spatio-temporal domain processing","volume":"594","author":"Zhang","year":"2026","journal-title":"Neuroscience"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shin, J., Kim, D.-W., M\u00fcller, K.-R., and Hwang, H.-J. (2018). Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses. Sensors, 18.","DOI":"10.3390\/s18061827"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hong, K.-S., Khan, M.J., and Hong, M.J. (2018). Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces. Front. Hum. Neurosci., 12.","DOI":"10.3389\/fnhum.2018.00246"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, J., Yu, K., Bi, Y., Ji, X., and Zhang, D. (2024). Strategic Integration: A Cross-Disciplinary Review of the fNIRS-EEG Dual-Modality Imaging System for Delivering Multimodal Neuroimaging to Applications. Brain Sci., 14.","DOI":"10.3390\/brainsci14101022"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1109\/TNSRE.2016.2628057","article-title":"Open Access Dataset for EEG+NIRS Single-Trial Classification","volume":"25","author":"Shin","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109619","DOI":"10.1016\/j.compeleceng.2024.109619","article-title":"Assessment of cognitive workload using simultaneous EEG and fNIRS: A comparison of feature combinations","volume":"119","author":"Ghasimi","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1109\/TMI.2025.3532480","article-title":"SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs","volume":"44","author":"Li","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4006110","DOI":"10.1109\/TIM.2023.3276509","article-title":"Hybrid EEG-fNIRS Brain Computer Interface Based on Common Spatial Pattern by Using EEG-Informed General Linear Model","volume":"72","author":"Gao","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qiu, L., Zhong, Y., Xie, Q., He, Z., Wang, X., Chen, Y., Zhan, C.A., and Pan, J. (2022). Multi-Modal Integration of EEG-fNIRS for Characterization of Brain Activity Evoked by Preferred Music. Front. Neurorobot., 16.","DOI":"10.3389\/fnbot.2022.823435"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yin, B., Yuan, X., and Wang, H. (2025). Classifier Design for EEG-fNIRS Bimodal Brain-Computer Interface Technology. Proceedings of the 2025 IEEE 20th Conference on Industrial Electronics and Applications (ICIEA), Yantai, China, 2025, IEEE.","DOI":"10.1109\/ICIEA65512.2025.11149172"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"IMAG.a.974","DOI":"10.1162\/IMAG.a.974","article-title":"Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging","volume":"3","author":"Codina","year":"2025","journal-title":"Imaging Neurosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Qiu, L., Zhong, Y., He, Z., and Pan, J. (2022). Improved Classification Performance of EEG-fNIRS Multimodal Brain-Computer Interface Based on Multi-Domain Features and Multi-Level Progressive Learning. Front. Hum. Neurosci., 16.","DOI":"10.3389\/fnhum.2022.973959"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"036028","DOI":"10.1088\/1741-2552\/aaaf82","article-title":"Deep Learning for Hybrid EEG-fNIRS Brain-Computer Interface: Application to Motor Imagery Classification","volume":"15","author":"Chiarelli","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113478","DOI":"10.1016\/j.asoc.2025.113478","article-title":"Advances in EEG-based emotion recognition: Challenges, methodologies, and future directions","volume":"180","author":"Chen","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"114097","DOI":"10.1016\/j.asoc.2025.114097","article-title":"Driver fatigue detection using EEG-based graph attention convolutional neural networks: An end-to-end learning approach with mutual information-driven connectivity","volume":"186","author":"Chen","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Saadati, M., Nelson, J., and Ayaz, H. (2020). Multimodal fNIRS-EEG Classification Using Deep Learning Algorithms for Brain-Computer Interface Purposes. Advances in Neuroergonomics and Cognitive Engineering, Springer.","DOI":"10.1007\/978-3-030-20473-0_21"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mughal, N.E., Khan, M.J., Khalil, K., Javed, K., Sajid, H., Naseer, N., Ghafoor, U., and Hong, K.-S. (2022). EEG-fNIRS-Based Hybrid Image Construction and Classification Using CNN-LSTM. Front. Neurorobot., 16.","DOI":"10.3389\/fnbot.2022.873239"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1007\/s11571-023-09986-4","article-title":"Deep Learning Networks Based Decision Fusion Model of EEG and fNIRS for Classification of Cognitive Tasks","volume":"18","author":"Rabbani","year":"2024","journal-title":"Cogn. Neurodyn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103417","DOI":"10.1016\/j.inffus.2025.103417","article-title":"Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces","volume":"125","author":"Wang","year":"2026","journal-title":"Inf. Fusion"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"62628","DOI":"10.1109\/ACCESS.2024.3394696","article-title":"Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey","volume":"12","author":"Keutayeva","year":"2024","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108705","DOI":"10.1016\/j.compbiomed.2024.108705","article-title":"Exploring the Frontier: Transformer-Based Models in EEG Signal Analysis for Brain-Computer Interfaces","volume":"178","author":"Pfeffer","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/JTEHM.2024.3448457","article-title":"Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging","volume":"12","author":"Bunterngchit","year":"2024","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhu, L., Huang, A., Zhang, J., and Yuan, P. (2025). Multimodal MBC-ATT: Cross-Modality Attentional Fusion of EEG-fNIRS for Cognitive State Decoding. Front. Hum. Neurosci., 19.","DOI":"10.3389\/fnhum.2025.1660532"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TNSRE.2022.3149899","article-title":"FGANet: fNIRS-Guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces","volume":"30","author":"Kwak","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_32","first-page":"2506312","article-title":"Fusion Analysis of EEG-fNIRS Multimodal Brain Signals: A Multitask Classification Algorithm Incorporating Spatial-Temporal Convolution and Dual Attention Mechanisms","volume":"74","author":"Shi","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/JBHI.2025.3594203","article-title":"IIMCNet: Intra- and Inter-Modality Correlation Network for Hybrid EEG-fNIRS Brain-Computer Interface","volume":"30","author":"Yuan","year":"2026","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, X., and Ming, D. (2023). Early-Stage Fusion of EEG and fNIRS Improves Classification of Motor Imagery. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.1062889"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, Z., Ji, H., Li, J., Liu, L., and Zhuang, J. (2022). Cross-Modal Transfer Learning from EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System. Front. Psychol., 13.","DOI":"10.3389\/fpsyg.2022.833007"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106943","DOI":"10.1016\/j.bspc.2024.106943","article-title":"E-FNet: A EEG-fNIRS Dual-Stream Model for Brain-Computer Interfaces","volume":"100","author":"Yu","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jiang, X., Bian, G.-B., and Tian, Z. (2019). Removal of Artifacts from EEG Signals: A Review. Sensors, 19.","DOI":"10.3390\/s19050987"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5104","DOI":"10.1364\/BOE.7.005104","article-title":"PHOEBE: A method for real time mapping of optodes-scalp coupling in functional near-infrared spectroscopy","volume":"7","author":"Pollonini","year":"2016","journal-title":"Biomed. Opt. Express"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lee, G., Jin, S.H., and An, J. (2018). Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network. Sensors, 18.","DOI":"10.3390\/s18092957"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Finnis, R., Mehmood, A., Holle, H., and Iqbal, J. (2025). Exploring Imagined Movement for Brain-Computer Interface Control: An fNIRS and EEG Review. Brain Sci., 15.","DOI":"10.3390\/brainsci15091013"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108594","DOI":"10.1016\/j.cmpb.2025.108594","article-title":"Neurophysiological Data Augmentation for EEG-fNIRS Multimodal Features Based on a Denoising Diffusion Probabilistic Model","volume":"261","author":"Chen","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_42","first-page":"5232","article-title":"Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity","volume":"23","author":"Fedus","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"0460a9","DOI":"10.1088\/1741-2552\/ac115d","article-title":"End-to-End Learnable EEG Channel Selection for Deep Neural Networks with Gumbel-Softmax","volume":"18","author":"Strypsteen","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Verelst, T., and Tuytelaars, T. (2020). Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00239"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7436","DOI":"10.1109\/TPAMI.2021.3117837","article-title":"Dynamic Neural Networks: A Survey","volume":"44","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103023","DOI":"10.1016\/j.inffus.2025.103023","article-title":"STA-Net: Spatial-Temporal Alignment Network for Hybrid EEG-fNIRS Decoding","volume":"119","author":"Liu","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, Z., Huang, G., Chen, Z., Liu, X., Liu, Y., and Hong, K.-S. (2026). Decoupled Bidirectional Spatio-Temporal Fusion Network for Hybrid EEG-fNIRS Cognitive Task Classification. Brain Sci., 16.","DOI":"10.3390\/brainsci16020241"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"122684","DOI":"10.1016\/j.ins.2025.122684","article-title":"SEAD-MGFE-Net: Schr\u00f6dinger Equation-Based Adaptive Dropout Multi-Granular Feature Enhancement Network for Conversational Aspect-Based Sentiment Quadruple Analysis","volume":"723","author":"Liu","year":"2026","journal-title":"Inf. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"180003","DOI":"10.1038\/sdata.2018.3","article-title":"Simultaneous Acquisition of EEG and NIRS During Cognitive Tasks for an Open Access Dataset","volume":"5","author":"Shin","year":"2018","journal-title":"Sci. Data"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhu, S., Wang, Q., Kan, C., Geng, A., Sui, Y., Zhuang, R., Zhu, Y., Wang, T., Zhu, L., and Guo, C. (2024). Age-related cerebral changes during different n-back tasks: A functional near-infrared spectroscopy study. Front. Aging Neurosci., 16.","DOI":"10.3389\/fnagi.2024.1437587"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"19419","DOI":"10.1038\/s41598-025-98653-1","article-title":"A Novel EEG Artifact Removal Algorithm Based on an Advanced Attention Mechanism","volume":"15","author":"Jiang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4217","DOI":"10.1364\/BOE.491610","article-title":"Test-Retest Reliability of fNIRS in Resting-State Cortical Activity and Brain Network Assessment in Stroke Patients","volume":"14","author":"Xu","year":"2023","journal-title":"Biomed. Opt. Express"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"116751","DOI":"10.1016\/j.chaos.2025.116751","article-title":"Decoding driving states based on normalized mutual information features and hyperparameter self-optimized Gaussian kernel-based radial basis function extreme learning machine","volume":"199","author":"Chen","year":"2025","journal-title":"Chaos Solitons Fractals"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4120","DOI":"10.1109\/JSEN.2025.3644160","article-title":"Nonlinear feature decomposition and deep temporal-spatial learning for single-channel sEMG-based lower limb motion recognition","volume":"26","author":"Wei","year":"2026","journal-title":"IEEE Sens. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105649","DOI":"10.1016\/j.rineng.2025.105649","article-title":"Subject based feature selection for hybrid brain computer interface using genetic algorithm and support vector machine","volume":"27","author":"Mateen","year":"2025","journal-title":"Results Eng."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Arif, A., Wang, Y., Yin, R., Zhang, X., and Helmy, A. (2024). EF-Net: Mental state recognition by analyzing multimodal EEG-fNIRS via CNN. Sensors, 24.","DOI":"10.3390\/s24061889"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T04:38:31Z","timestamp":1780547911000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,27]]},"references-count":56,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26113402"],"URL":"https:\/\/doi.org\/10.3390\/s26113402","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,27]]}}}