{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:57:35Z","timestamp":1765961855130,"version":"3.37.3"},"reference-count":67,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62101189"],"award-info":[{"award-number":["62101189"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"crossref","award":["LTGC23F010001"],"award-info":[{"award-number":["LTGC23F010001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Basic Research Project of Leading Technology of Jiangsu Province","award":["BK20192004"],"award-info":[{"award-number":["BK20192004"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Deep learning technique has been widely used for decoding motor related electroencephalography (EEG) signals, which has considerably driven the development of motor related brain\u2013computer interfaces (BCIs). However, traditional convolutional neural networks (CNNs) cannot fully represent spatial topology information and dynamic temporal characteristics of multi-channel EEG signals, resulting in limited decoding accuracy. To address such challenges, a novel multi-scale multi-graph embedding CNN (MSMGE-CNN) is proposed in this study. The proposed MSMGE-CNN contains two crucial components: multi-scale time convolution and multi-graph embedding. Specifically, we design a multi-branch CNN architecture with mixed-scale time convolutions based on EEGNet to sufficiently extract robust time domain features. Afterward, we embed multi-graph information obtained based on physical distance proximity and functional connectivity of multi-channel EEG signals into the time-domain features to capture rich spatial topological dependencies via multi-graph convolution operation. We extensively evaluated the proposed method on three benchmark EEG datasets commonly used for motor imagery\/execution (MI\/ME) classification and obtained accuracies of 79.59% (BCICIV-2a Dataset), 69.77% (OpenBMI Dataset) and 96.34% (High Gamma Dataset), respectively. These results powerfully demonstrate that MSMGE-CNN outperforms several state-of-the-art algorithms. In addition, we further conducted a series of ablation experiments to validate the rationality of our network architecture. Overall, the proposed MSMGE-CNN method dramatically improves the accuracy and robustness of MI\/ME-EEG decoding, which can effectively enhance the performance of motor related BCI system.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad9135","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T22:58:51Z","timestamp":1731365931000},"page":"045047","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["MSMGE-CNN: a multi-scale multi-graph embedding convolutional neural network for motor related EEG decoding"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0545-1998","authenticated-orcid":true,"given":"Binren","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8437-2412","authenticated-orcid":true,"given":"Minmin","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wenzhe","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhenzhen","family":"Sheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7714-9645","authenticated-orcid":true,"given":"Baoguo","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Wenjun","family":"Hu","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"mlstad9135bib1","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neucom.2016.08.121","article-title":"Age-related differences in SSVEP-based BCI performance","volume":"250","author":"Volosyak","year":"2017","journal-title":"Neurocomputing"},{"key":"mlstad9135bib2","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1109\/TSMC.2015.2450680","article-title":"Common bayesian network for classification of eeg-based multiclass motor imagery bci","volume":"46","author":"Lianghua","year":"2015","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"mlstad9135bib3","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1002\/ana.24390","article-title":"Brain\u2013computer interface boosts motor imagery practice during stroke recovery","volume":"77","author":"Pichiorri","year":"2015","journal-title":"Ann. Neurol."},{"key":"mlstad9135bib4","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1109\/JPROC.2015.2415800","article-title":"Brain\u2013computer interface for neurorehabilitation of upper limb after stroke","volume":"vol 103","author":"Keng Ang","year":"2015"},{"key":"mlstad9135bib5","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.irbm.2018.02.001","article-title":"Eeg based brain computer interface for controlling a robot arm movement through thought","volume":"39","author":"Rihab Bousseta","year":"2018","journal-title":"IRBM"},{"key":"mlstad9135bib6","doi-asserted-by":"publisher","first-page":"eaaw6844","DOI":"10.1126\/scirobotics.aaw6844","article-title":"Noninvasive neuroimaging enhances continuous neural tracking for robotic device control","volume":"4","author":"Edelman","year":"2019","journal-title":"Sci. Robot."},{"key":"mlstad9135bib7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-018-0545-x","article-title":"Towards bci-actuated smart wheelchair system","volume":"17","author":"Tang","year":"2018","journal-title":"Biomed. Eng. Online"},{"key":"mlstad9135bib8","doi-asserted-by":"publisher","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":"mlstad9135bib9","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces: a 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"mlstad9135bib10","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac74e0","article-title":"How to successfully classify EEG in motor imagery bci: A metrological analysis of the state of the art","volume":"19","author":"Arpaia","year":"2022","journal-title":"J. Neural Eng."},{"key":"mlstad9135bib11","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1109\/86.895946","article-title":"Optimal spatial filtering of single trial EEG during imagined hand movement","volume":"8","author":"Ramoser","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"mlstad9135bib12","first-page":"2390","article-title":"Filter bank common spatial pattern (fbcsp) in brain-computer interface","author":"Keng Ang","year":"2008"},{"key":"mlstad9135bib13","doi-asserted-by":"publisher","first-page":"2730","DOI":"10.1109\/TBME.2009.2026181","article-title":"Chiew Tong Lau, A Prasad Vinod and Kai Keng Ang. A new discriminative common spatial pattern method for motor imagery brain\u2013computer interfaces","volume":"56","author":"Thomas","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"mlstad9135bib14","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/TNSRE.2017.2757519","article-title":"Filter bank regularized common spatial pattern ensemble for small sample motor imagery classification","volume":"26","author":"Park","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"mlstad9135bib15","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101994","article-title":"An adaptive multi-domain feature joint optimization framework based on composite kernels and ant colony optimization for motor imagery EEG classification","volume":"61","author":"Miao","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"mlstad9135bib16","doi-asserted-by":"publisher","first-page":"15632","DOI":"10.1109\/TPAMI.2023.3299568","article-title":"Sparse bayesian learning for end-to-end EEG decoding","volume":"45","author":"Wang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"mlstad9135bib17","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abc902","article-title":"A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers","volume":"18","author":"Zhang","year":"2021","journal-title":"J. Neural Eng."},{"key":"mlstad9135bib18","first-page":"503","article-title":"Deep learning of multifractal attributes from motor imagery induced EEG","author":"Junhua","year":"2014"},{"key":"mlstad9135bib19","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/14\/1\/016003","article-title":"A novel deep learning approach for classification of EEG motor imagery signals","volume":"14","author":"Rezaei Tabar","year":"2016","journal-title":"J. Neural Eng."},{"key":"mlstad9135bib20","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ad48bc","article-title":"Self-supervised motor imagery EEG recognition model based on 1-d mtcnn-lstm network","volume":"21","author":"Cunlin","year":"2024","journal-title":"J. Neural Eng."},{"key":"mlstad9135bib21","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","article-title":"EEG conformer: convolutional transformer for EEG decoding and visualization","volume":"31","author":"Song","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"mlstad9135bib22","doi-asserted-by":"publisher","first-page":"4039","DOI":"10.1109\/TNNLS.2020.3016666","article-title":"Generative adversarial networks-based data augmentation for brain\u2013computer interface","volume":"32","author":"Fahimi","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"mlstad9135bib23","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Tibor Schirrmeister","year":"2017","journal-title":"Human Brain Mapping"},{"key":"mlstad9135bib24","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","article-title":"Eegnet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"mlstad9135bib25","doi-asserted-by":"publisher","DOI":"10.1111\/ene.15181","article-title":"Fbcnet: A multi-view convolutional neural network for brain-computer interface","author":"Mane","year":"2021"},{"key":"mlstad9135bib26","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/TBME.2022.3193277","article-title":"Fbmsnet: a filter-bank multi-scale convolutional neural network for EEG-based motor imagery decoding","volume":"70","author":"Liu","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"mlstad9135bib27","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1109\/TAFFC.2020.2994159","article-title":"Eeg-based emotion recognition using regularized graph neural networks","volume":"13","author":"Zhong","year":"2020","journal-title":"IEEE Trans. Affective Comput."},{"key":"mlstad9135bib28","doi-asserted-by":"publisher","first-page":"93711","DOI":"10.1109\/ACCESS.2019.2927768","article-title":"Phase-locking value based graph convolutional neural networks for emotion recognition","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"mlstad9135bib29","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","article-title":"EEG emotion recognition using dynamical graph convolutional neural networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affective Comput."},{"key":"mlstad9135bib30","doi-asserted-by":"publisher","first-page":"7312","DOI":"10.1109\/TNNLS.2022.3202569","article-title":"Gcns-net: a graph convolutional neural network approach for decoding time-resolved EEG motor imagery signals","volume":"35","author":"Hou","year":"2022"},{"key":"mlstad9135bib31","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1016\/j.bbe.2022.08.003","article-title":"Eeg_genet: a feature-level graph embedding method for motor imagery classification based on EEG signals","volume":"42","author":"Wang","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"key":"mlstad9135bib32","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab405f","article-title":"Hs-cnn: a cnn with hybrid convolution scale for EEG motor imagery classification","volume":"17","author":"Dai","year":"2020","journal-title":"J. Neural Eng."},{"key":"mlstad9135bib33","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.patcog.2015.11.022","article-title":"Human action recognition with graph-based multiple-instance learning","volume":"53","author":"Yang","year":"2016","journal-title":"Pattern Recognit."},{"key":"mlstad9135bib34","first-page":"659","article-title":"Graph-based point-of-interest recommendation with geographical and temporal influences","author":"Yuan","year":"2014"},{"key":"mlstad9135bib35","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","article-title":"Graph based anomaly detection and description: a survey","volume":"29","author":"Akoglu","year":"2015","journal-title":"Data Mining Knowl. Discov."},{"key":"mlstad9135bib36","first-page":"p 29","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"Defferrard","year":"2016"},{"article-title":"Semi-supervised classification with graph convolutional networks","year":"2016","author":"Kipf","key":"mlstad9135bib37"},{"article-title":"Revisiting graph neural networks: All we have is low-pass filters","year":"2019","author":"Maehara","key":"mlstad9135bib38"},{"key":"mlstad9135bib39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2023.109969","article-title":"Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems","volume":"399","author":"Sun","year":"2023","journal-title":"J. Neurosci. Methods"},{"article-title":"Mutualgraphnet: a novel model for motor imagery classification","year":"2021","author":"Yan","key":"mlstad9135bib40"},{"key":"mlstad9135bib41","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1007\/s11571-022-09892-1","article-title":"Novel channel selection model based on graph convolutional network for motor imagery","volume":"17","author":"Liang","year":"2023","journal-title":"Cogn. Neurodyn."},{"key":"mlstad9135bib42","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1109\/LSP.2021.3049683","article-title":"Adaptive spatiotemporal graph convolutional networks for motor imagery classification","volume":"28","author":"Sun","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"mlstad9135bib43","doi-asserted-by":"publisher","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 Inf."},{"article-title":"Overfeat: Integrated recognition, localization and detection using convolutional networks","year":"2013","author":"Sermanet","key":"mlstad9135bib44"},{"key":"mlstad9135bib45","first-page":"3066","article-title":"Eeg-based video identification using graph signal modeling and graph convolutional neural network","author":"Jang","year":"2018"},{"key":"mlstad9135bib46","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s11571-018-9495-z","article-title":"Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness","volume":"12","author":"Chen","year":"2018","journal-title":"Cogn. Neurodyn."},{"key":"mlstad9135bib47","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1093\/ijnp\/pyac018","article-title":"Effects of transcranial direct current stimulation on attentional bias to methamphetamine cues and its association with EEG-derived functional brain network topology","volume":"25","author":"Khajehpour","year":"2022","journal-title":"Int. J. Neuropsychopharmacol."},{"key":"mlstad9135bib48","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1109\/TNSRE.2020.3014951","article-title":"Topological network analysis of early alzheimer\u2019s disease based on resting-state EEG","volume":"28","author":"Duan","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"mlstad9135bib49","doi-asserted-by":"publisher","first-page":"2123","DOI":"10.1016\/j.patcog.2011.04.034","article-title":"Brain computer interface control via functional connectivity dynamics","volume":"45","author":"Daly","year":"2012","journal-title":"Pattern Recognit."},{"key":"mlstad9135bib50","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.neulet.2017.04.031","article-title":"Infants and adults have similar regional functional brain organization for the perception of emotions","volume":"650","author":"Rotem-Kohavi","year":"2017","journal-title":"Neurosci. Lett."},{"key":"mlstad9135bib51","first-page":"1061","article-title":"EEG-GNN: Graph neural networks for classification of electroencephalogram (EEG) signals","author":"Demir","year":"2021"},{"key":"mlstad9135bib52","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1109\/TNSRE.2019.2894423","article-title":"Graph theory analysis of functional connectivity in major depression disorder with high-density resting state EEG data","volume":"27","author":"Sun","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"mlstad9135bib53","doi-asserted-by":"publisher","first-page":"giz002","DOI":"10.1093\/gigascience\/giz002","article-title":"Eeg dataset and openbmi toolbox for three BCI paradigms: an investigation into BCI illiteracy","volume":"8","author":"Min-Ho Lee","year":"2019","journal-title":"GigaScience"},{"key":"mlstad9135bib54","first-page":"1","article-title":"BCI competition 2008\u2013graz data set a Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces) Graz University of Technology","volume":"vol 16","author":"Brunner","year":"2008"},{"article-title":"Adam: a method for stochastic optimization","year":"2014","author":"Kingma","key":"mlstad9135bib55"},{"key":"mlstad9135bib56","doi-asserted-by":"publisher","first-page":"540","DOI":"10.4097\/kjae.2015.68.6.540","article-title":"T test as a parametric statistic","volume":"68","author":"Kyun Kim","year":"2015","journal-title":"Korean J. Anesthesiol."},{"key":"mlstad9135bib57","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1002\/bimj.200390056","article-title":"On the use of the shapiro-wilk test in two-stage adaptive inference for paired data from moderate to very heavy tailed distributions","volume":"45","author":"Freidlin","year":"2003","journal-title":"Biomet. J."},{"key":"mlstad9135bib58","first-page":"191","article-title":"Introduction to Wilcoxon (1945) Individual Comparisons by Ranking Methods","author":"Noether","year":"1992"},{"key":"mlstad9135bib59","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"mlstad9135bib60","first-page":"5565","article-title":"A multi-domain adaptive graph convolutional network for EEG-based emotion recognition","author":"Rui","year":"2021"},{"key":"mlstad9135bib61","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"vol 30","author":"Scott","year":"2017"},{"key":"mlstad9135bib62","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.jneumeth.2016.10.008","article-title":"Interpretable deep neural networks for single-trial EEG classification","volume":"274","author":"Sturm","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"mlstad9135bib63","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neuroimage.2005.12.003","article-title":"Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks","volume":"31","author":"Pfurtscheller","year":"2006","journal-title":"NeuroImage"},{"key":"mlstad9135bib64","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/0304-3940(93)90886-P","article-title":"40-hz oscillations during motor behavior in man","volume":"164","author":"Pfurtscheller","year":"1993","journal-title":"Neurosci. Lett."},{"key":"mlstad9135bib65","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abed81","article-title":"Eeg-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification","volume":"18","author":"Zhang","year":"2021","journal-title":"J. Neural Eng."},{"key":"mlstad9135bib66","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.neunet.2022.06.008","article-title":"Transfer learning for motor imagery based brain\u2013computer interfaces: a tutorial","volume":"153","author":"Dongrui","year":"2022","journal-title":"Neural Netw."},{"key":"mlstad9135bib67","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104496","article-title":"Automatic sleep scoring using patient-specific ensemble models and knowledge distillation for EAR-EEG data","volume":"81","author":"Borup","year":"2023","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9135","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9135\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9135","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9135\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9135\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9135\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9135\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9135\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T13:30:28Z","timestamp":1732282228000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":67,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,11,22]]},"published-print":{"date-parts":[[2024,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad9135","relation":{},"ISSN":["2632-2153"],"issn-type":[{"type":"electronic","value":"2632-2153"}],"subject":[],"published":{"date-parts":[[2024,11,22]]},"assertion":[{"value":"MSMGE-CNN: a multi-scale multi-graph embedding convolutional neural network for motor related EEG decoding","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-08-07","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-11-11","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-11-22","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}