{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:18:17Z","timestamp":1771658297594,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Education, University and Research (MIUR)","award":["Dipartimenti di Eccellenza"],"award-info":[{"award-number":["Dipartimenti di Eccellenza"]}]},{"name":"Italian Ministry of Education, University and Research (MIUR)","award":["Ricerca&Sviluppo"],"award-info":[{"award-number":["Ricerca&Sviluppo"]}]},{"name":"Fondazione Cariverona","award":["Dipartimenti di Eccellenza"],"award-info":[{"award-number":["Dipartimenti di Eccellenza"]}]},{"name":"Fondazione Cariverona","award":["Ricerca&Sviluppo"],"award-info":[{"award-number":["Ricerca&Sviluppo"]}]},{"name":"REACT EU-PON Ricerca e Innovazione","award":["Dipartimenti di Eccellenza"],"award-info":[{"award-number":["Dipartimenti di Eccellenza"]}]},{"name":"REACT EU-PON Ricerca e Innovazione","award":["Ricerca&Sviluppo"],"award-info":[{"award-number":["Ricerca&Sviluppo"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>(1) Background: in the field of motor-imagery brain\u2013computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.<\/jats:p>","DOI":"10.3390\/s23177520","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T10:30:52Z","timestamp":1693391452000},"page":"7520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain\u2013Computer Interface Performance"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2974-1569","authenticated-orcid":false,"given":"Ilaria","family":"Siviero","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6889-3461","authenticated-orcid":false,"given":"Gloria","family":"Menegaz","sequence":"additional","affiliation":[{"name":"Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8605-803X","authenticated-orcid":false,"given":"Silvia Francesca","family":"Storti","sequence":"additional","affiliation":[{"name":"Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wolpaw, J.R. (2007, January 15\u201317). Brain-computer interfaces (BCIs) for communication and control. Proceedings of the 9th International ACM SIGACCESS Conference on Computers and Accessibility, Tempe, AZ, USA.","DOI":"10.1145\/1296843.1296845"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1007\/s00702-007-0763-z","article-title":"Motor imagery and action observation: Cognitive tools for rehabilitation","volume":"114","author":"Mulder","year":"2007","journal-title":"J. Neural Transm."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1016\/j.mri.2010.06.030","article-title":"Brain oscillatory activity during motor imagery in EEG-fMRI coregistration","volume":"28","author":"Formaggio","year":"2010","journal-title":"Magn. Reson. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"011001","DOI":"10.1088\/1741-2552\/aaf12e","article-title":"A comprehensive review of EEG-based brain\u2013computer interface paradigms","volume":"16","author":"Abiri","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Padfield, N., Zabalza, J., Zhao, H., Masero, V., and Ren, J. (2019). EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges. Sensors, 19.","DOI":"10.3390\/s19061423"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1162\/NECO_a_00838","article-title":"Electroencephalographic motor imagery brain connectivity analysis for BCI: A review","volume":"28","author":"Hamedi","year":"2016","journal-title":"Neural Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Singh, A., Hussain, A.A., Lal, S., and Guesgen, H.W. (2021). A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface. Sensors, 21.","DOI":"10.3390\/s21062173"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"046014","DOI":"10.1088\/1741-2560\/10\/4\/046014","article-title":"Multiresolution analysis over simple graphs for brain computer interfaces","volume":"10","author":"Gan","year":"2013","journal-title":"J. Neural Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"016015","DOI":"10.1088\/1741-2552\/abce70","article-title":"Early classification of motor tasks using dynamic functional connectivity graphs from EEG","volume":"18","author":"Shamsi","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102584","DOI":"10.1016\/j.bspc.2021.102584","article-title":"EEG motor imagery classification using dynamic connectivity patterns and convolutional autoencoder","volume":"68","author":"Mirzaei","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/TBME.2019.2913928","article-title":"Electrophysiological brain connectivity: Theory and implementation","volume":"66","author":"He","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1002\/hbm.25683","article-title":"Brain functional and effective connectivity based on electroencephalography recordings: A review","volume":"43","author":"Cao","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s11571-021-09689-8","article-title":"A survey of brain network analysis by electroencephalographic signals","volume":"16","author":"Luo","year":"2022","journal-title":"Cogn. Neurodynamics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TNSRE.2015.2458982","article-title":"Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation","volume":"24","author":"Parvez","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1038\/nrn3214","article-title":"The economy of brain network organization","volume":"13","author":"Bullmore","year":"2012","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1002\/hbm.460020107","article-title":"Functional and effective connectivity in neuroimaging: A synthesis","volume":"2","author":"Friston","year":"1994","journal-title":"Hum. Brain Mapp."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.1038\/s41598-022-06573-1","article-title":"Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals","volume":"12","author":"Ioannides","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"011001","DOI":"10.1088\/1741-2552\/abc760","article-title":"Network-based brain\u2013computer interfaces: Principles and applications","volume":"18","author":"Cattai","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.plrev.2018.10.001","article-title":"Network neuroscience for optimizing brain\u2013computer interfaces","volume":"31","author":"Fallani","year":"2019","journal-title":"Phys. Life Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/nn.4502","article-title":"Network neuroscience","volume":"20","author":"Bassett","year":"2017","journal-title":"Nat. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1223955","DOI":"10.3389\/fnins.2023.1223955","article-title":"Chasing brain dynamics at their speed: What can time-varying functional connectivity tell us about brain function?","volume":"17","author":"Rubega","year":"2023","journal-title":"Front. Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1109\/THMS.2021.3115094","article-title":"A Systematic Review on Motor-Imagery Brain-Connectivity-Based Computer Interfaces","volume":"51","author":"Brusini","year":"2021","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"065026","DOI":"10.1088\/2057-1976\/ab5145","article-title":"A correntropy-based classifier for motor imagery brain-computer interfaces","volume":"5","author":"Uribe","year":"2019","journal-title":"Biomed. Phys. Eng. Express"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Feng, Z., Qian, L., Hu, H., and Sun, Y. (2020, January 11\u201314). Functional connectivity for motor imaginary recognition in brain-computer interface. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada.","DOI":"10.1109\/SMC42975.2020.9283075"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102501","DOI":"10.1016\/j.bspc.2021.102501","article-title":"Scattering convolutional network based predictive model for cognitive activity of brain using empirical wavelet decomposition","volume":"66","author":"Jayalakshmy","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Siviero, I., Brusini, L., Menegaz, G., and Storti, S.F. (2022, January 27\u201330). Motor-imagery EEG signal decoding using multichannel-empirical wavelet transform for brain computer interfaces. Proceedings of the 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece.","DOI":"10.1109\/BHI56158.2022.9926766"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1109\/TNSRE.2023.3241241","article-title":"Classification of Motor-Imagery Tasks Using a Large EEG Dataset by Fusing Classifiers Learning on Wavelet-Scattering Features","volume":"31","author":"Pham","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/TPAMI.2012.230","article-title":"Invariant scattering convolution networks","volume":"35","author":"Bruna","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","first-page":"20150203","article-title":"Understanding deep convolutional networks","volume":"374","author":"Mallat","year":"2016","journal-title":"Philos. Trans. R. Soc. Math. Phys. Eng. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/0166-4328(95)00225-1","article-title":"The neurophysiological basis of motor imagery","volume":"77","author":"Decety","year":"1996","journal-title":"Behav. Brain Res."},{"key":"ref_31","unstructured":"Eccles, J.C. (1953). The Neurophysiological Basis of Mind: The Principles of Neurophysiology, Oxford University Press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/1743-0003-10-24","article-title":"Modulation of event-related desynchronization in robot-assisted hand performance: Brain oscillatory changes in active, passive and imagined movements","volume":"10","author":"Formaggio","year":"2013","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"126698","DOI":"10.1109\/ACCESS.2021.3110882","article-title":"A Novel Channel selection method for BCI classification using Dynamic Channel relevance","volume":"9","author":"Tiwari","year":"2021","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3085","DOI":"10.1007\/s00500-015-1937-5","article-title":"Adaptive learning with covariate shift-detection for motor imagery-based brain\u2013computer interface","volume":"20","author":"Raza","year":"2016","journal-title":"Soft Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3389\/fnins.2012.00151","article-title":"Multi-class motor imagery EEG decoding for brain-computer interfaces","volume":"6","author":"Wang","year":"2012","journal-title":"Front. Neurosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"155590","DOI":"10.1109\/ACCESS.2020.3018962","article-title":"Diverse feature blend based on filter-bank common spatial pattern and brain functional connectivity for multiple motor imagery detection","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"S795","DOI":"10.3233\/THC-161212","article-title":"Discrimination of motor imagery tasks via information flow pattern of brain connectivity","volume":"24","author":"Liang","year":"2016","journal-title":"Technol. Health Care"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.3389\/fnins.2019.01277","article-title":"A Data-Driven Measure of Effective Connectivity Based on Renyi\u2019s \u03b1-Entropy","volume":"13","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1007\/s11517-019-01989-w","article-title":"Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces","volume":"57","author":"Rodrigues","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102028","DOI":"10.1016\/j.bspc.2020.102028","article-title":"Motor imagery classification by active source dynamics","volume":"61","author":"Rajabioun","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.neuroimage.2018.03.032","article-title":"A subject-transfer framework for obviating inter-and intra-subject variability in EEG-based drowsiness detection","volume":"174","author":"Wei","year":"2018","journal-title":"NeuroImage"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3389\/fncom.2019.00087","article-title":"Intra-and inter-subject variability in EEG-based sensorimotor brain computer interface: A review","volume":"13","author":"Saha","year":"2020","journal-title":"Front. Comput. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/S0031-3203(02)00262-5","article-title":"Feature fusion: Parallel strategy vs. serial strategy","volume":"36","author":"Yang","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"026032","DOI":"10.1088\/1741-2552\/ab0328","article-title":"Feature extraction of four-class motor imagery EEG signals based on functional brain network","volume":"16","author":"Ai","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/TCDS.2016.2555952","article-title":"Decoding EEG in cognitive tasks with time-frequency and connectivity masks","volume":"8","author":"Li","year":"2016","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_46","first-page":"1","article-title":"BCI Competition 2008\u2013Graz data set A","volume":"16","author":"Brunner","year":"2008","journal-title":"Inst. Knowl. Discov. Graz Univ. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s11517-018-1875-3","article-title":"A hierarchical semi-supervised extreme learning machine method for EEG recognition","volume":"57","author":"She","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/TBME.2011.2172210","article-title":"Multiclass brain\u2013computer interface classification by Riemannian geometry","volume":"59","author":"Barachant","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1002\/hbm.20346","article-title":"Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources","volume":"28","author":"Stam","year":"2007","journal-title":"Hum. Brain Mapp."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ins.2019.01.053","article-title":"A novel method for classification of BCI multi-class motor imagery task based on Dempster\u2013Shafer theory","volume":"484","author":"Razi","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1109\/JBHI.2022.3146274","article-title":"Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI","volume":"26","author":"Fang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_52","first-page":"469","article-title":"Nonstationary brain source separation for multiclass motor imagery","volume":"57","author":"Congedo","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jneumeth.2016.12.010","article-title":"Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Na\u00efve Bayesian Classifier-based approach","volume":"278","author":"Miao","year":"2017","journal-title":"J. Neurosci. Methods"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.eswa.2017.11.007","article-title":"A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry","volume":"95","author":"Gaur","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ghanbar, K.D., Rezaii, T.Y., Farzamnia, A., and Saad, I. (2021). Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0248511"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"224","DOI":"10.26599\/BSA.2020.9050021","article-title":"Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions","volume":"6","author":"Zhang","year":"2020","journal-title":"Brain Sci. Adv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2570","DOI":"10.1109\/JBHI.2020.2967128","article-title":"Motor imagery classification via temporal attention cues of graph embedded EEG signals","volume":"24","author":"Zhang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1109\/JSTSP.2016.2602007","article-title":"Event-related functional network identification: Application to EEG classification","volume":"10","author":"Gonuguntla","year":"2016","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"342","DOI":"10.3389\/fnhum.2021.602723","article-title":"Neural Kinesthetic Contribution to Motor Imagery of Body Parts: Tongue, Hands, and Feet","volume":"15","author":"Giannopulu","year":"2021","journal-title":"Front. Hum. Neurosci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0304-3940(02)00826-1","article-title":"Somatotopic mapping of the human primary sensorimotor cortex during motor imagery and motor execution by functional magnetic resonance imaging","volume":"331","author":"Stippich","year":"2002","journal-title":"Neurosci. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Bera, S., Roy, R., Sikdar, D., Kar, A., Mukhopadhyay, R., and Mahadevappal, M. (2018, January 18\u201321). A randomised ensemble learning approach for multiclass motor imagery classification using error correcting output coding. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513421"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TNSRE.2021.3139095","article-title":"A novel classification framework using the graph representations of electroencephalogram for motor imagery based brain-computer interface","volume":"30","author":"Jin","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TCDS.2020.3007453","article-title":"Transfer learning for EEG-based brain\u2013computer interfaces: A review of progress made since 2016","volume":"14","author":"Wu","year":"2020","journal-title":"IEEE Trans. Cogn. Dev. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7520\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:42:39Z","timestamp":1760128959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,30]]},"references-count":63,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177520"],"URL":"https:\/\/doi.org\/10.3390\/s23177520","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,30]]}}}