{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:20:46Z","timestamp":1781842846187,"version":"3.54.5"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People\u2019s Republic of China","doi-asserted-by":"publisher","award":["21A13022003"],"award-info":[{"award-number":["21A13022003"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY19F030010"],"award-info":[{"award-number":["LY19F030010"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Social Science Fund","award":["20NDJC216YB"],"award-info":[{"award-number":["20NDJC216YB"]}]},{"DOI":"10.13039\/100007834","name":"Natural Science Foundation of Ningbo","doi-asserted-by":"publisher","award":["2019A610083"],"award-info":[{"award-number":["2019A610083"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Educational Science Scheme 2021","award":["GH2021642"],"award-info":[{"award-number":["GH2021642"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72071049"],"award-info":[{"award-number":["72071049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    It has been a challenge for solving the motor imagery classification problem in the brain informatics area. Accuracy and efficiency are the major obstacles for motor imagery analysis in the past decades since the computational capability and algorithmic availability cannot satisfy complex brain signal analysis. In recent years, the rapid development of machine learning (ML) methods has empowered people to tackle the motor imagery classification problem with more efficient methods. Among various ML methods, the Graph neural networks (GNNs) method has shown its efficiency and accuracy in dealing with inter-related complex networks. The use of GNN provides new possibilities for feature extraction from brain structure connection. In this paper, we proposed a new model called MCGNet\n                    <jats:sup>+<\/jats:sup>\n                    , which improves the performance of our previous model MutualGraphNet. In this latest model, the mutual information of the input columns forms the initial adjacency matrix for the cosine similarity calculation between columns to generate a new adjacency matrix in each iteration. The dynamic adjacency matrix combined with the spatial temporal graph convolution network (ST-GCN) has better performance than the unchanged matrix model. The experimental results indicate that MCGNet\n                    <jats:sup>+<\/jats:sup>\n                    is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.\n                  <\/jats:p>","DOI":"10.1186\/s40708-021-00151-3","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T11:59:20Z","timestamp":1643716760000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["MCGNet+: an improved motor imagery classification based on cosine similarity"],"prefix":"10.1186","volume":"9","author":[{"given":"Yan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Taniar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5033-1866","authenticated-orcid":false,"given":"Haolan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,1]]},"reference":[{"key":"151_CR1","doi-asserted-by":"crossref","unstructured":"Song Y, Wang D, Yue K, Zheng N, Shen Z-JM (2019) Eeg-based motor imagery classification with deep multi-task learning. In: 2019 International Joint Conference on Neural Networks (IJCNN), 1\u20138 (2019)","DOI":"10.1109\/IJCNN.2019.8852362"},{"issue":"6","key":"151_CR2","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","volume":"113","author":"JR Wolpaw","year":"2002","unstructured":"Wolpaw JR, Birbaumer N, Mcfarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain\u2013computer interfaces for communication and control. Suppl Clin Neurophysiol 113(6):767\u2013791","journal-title":"Suppl Clin Neurophysiol"},{"issue":"2","key":"151_CR3","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.neuroimage.2007.01.051","volume":"37","author":"B Blankertz","year":"2007","unstructured":"Blankertz B, Dornhege G, Krauledat M, M\u00fcller K, Curio G (2007) The non-invasive berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. NeuroImage 37(2):539\u2013550","journal-title":"NeuroImage"},{"issue":"3","key":"151_CR4","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/0013-4694(91)90040-B","volume":"78","author":"JR Wolpaw","year":"1991","unstructured":"Wolpaw JR, Mcfarland DJ, Neat GW, Forneris CA (1991) An eeg-based brain\u2013computer interface for cursor control. Electroencephalogr Clin Neurophysiol 78(3):252\u2013259","journal-title":"Electroencephalogr Clin Neurophysiol"},{"issue":"3","key":"151_CR5","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1037\/0033-2909.127.3.358","volume":"127","author":"A K\u00fcbler","year":"2001","unstructured":"K\u00fcbler A, Kotchoubey B, Kaiser J, Wolpaw JR, Birbaumer N (2001) Brain\u2013computer communication: unlocking the locked in. Psycholog Bull 127(3):358\u2013375","journal-title":"Psycholog Bull"},{"issue":"2","key":"151_CR6","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1109\/TNSRE.2006.875637","volume":"14","author":"DJ Mcfarland","year":"2006","unstructured":"Mcfarland DJ, Anderson CW, Muller KR, Schlogl A, Krusienski DJ (2006) Bci meeting 2005-workshop on bci signal processing: feature extraction and translation. IEEE Trans Neural Syst Rehabil Eng 14(2):135\u2013138","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"151_CR7","doi-asserted-by":"crossref","unstructured":"Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo Rakotomamonjy (2018) A review of classification algorithms for eeg-based brain\u2013computer interfaces: a 10 year update. J Neural Eng","DOI":"10.1088\/1741-2552\/aab2f2"},{"issue":"2","key":"151_CR8","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1088\/1741-2560\/4\/2\/R03","volume":"4","author":"A Bashashati","year":"2007","unstructured":"Bashashati A, Fatourechi M, Ward RK, Birch GE (2007) A survey of signal processing algorithms in brain\u2013computer interfaces based on electrical brain signals. J Neural Eng 4(2):32","journal-title":"J Neural Eng"},{"issue":"13","key":"151_CR9","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1109\/JPROC.2012.2185009","volume":"100","author":"S Makeig","year":"2012","unstructured":"Makeig S, Kothe C, Mullen T, Bigdely-Shamlo N, Zhang Z, Kreutz-Delgado K (2012) Evolving signal processing for brain\u2013computer interfaces. Proc IEEE 100(13):1567\u20131584","journal-title":"Proc IEEE"},{"key":"151_CR10","doi-asserted-by":"crossref","unstructured":"Lotte F (2014) A tutorial on eeg signal processing techniques for mental state recognition in brain\u2013computer interfaces. In: Miranda E, Castet J (eds) Guide to brain-computer music interfacing. p. 133\u2013161","DOI":"10.1007\/978-1-4471-6584-2_7"},{"issue":"1","key":"151_CR11","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/MSP.2008.4408442","volume":"25","author":"A Kachenoura","year":"2008","unstructured":"Kachenoura A, Albera L, Senhadji L, Comon P (2008) Ica: a potential tool for bci systems. Signal Process Magazine IEEE 25(1):57\u201368","journal-title":"Signal Process Magazine IEEE"},{"key":"151_CR12","first-page":"39","volume":"6","author":"AK Keng","year":"2012","unstructured":"Keng AK, Yang CZ, Wang C, Guan C, Zhang H (2012) Filter bank common spatial pattern algorithm on bci competition iv datasets 2a and 2b. Front Neurosci 6:39","journal-title":"Front Neurosci"},{"issue":"7","key":"151_CR13","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1109\/TBME.2015.2402252","volume":"62","author":"H Woehrle","year":"2015","unstructured":"Woehrle H, Krell MM, Straube S, Su KK, Kirchner F (2015) An adaptive spatial filter for user-independent single trial detection of event-related potentials. IEEE Trans Bio-Med Eng 62(7):1696\u20131705","journal-title":"IEEE Trans Bio-Med Eng"},{"issue":"11","key":"151_CR14","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","volume":"38","author":"RT Schirrmeister","year":"2017","unstructured":"Schirrmeister RT, Springenberg JT, Fiederer L, Glasstetter M, Eggensperger K, Tangermann M, Hutter F, Burgard W, Ball T (2017) Deep learning with convolutional neural networks for eeg decoding and visualization. Human Brain Mapping 38(11):5391\u20135420","journal-title":"Human Brain Mapping"},{"issue":"5","key":"151_CR15","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2016","unstructured":"Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ (2016) Eegnet: a compact convolutional network for eeg-based brain\u2013computer interfaces. J Neural Eng 15(5):056013\u2013105601317","journal-title":"J Neural Eng"},{"key":"151_CR16","doi-asserted-by":"crossref","unstructured":"Jia Z, Lin Y, Wang J, Zhou R, Zhao Y (2020) Graphsleepnet: adaptive spatial-temporal graph convolutional networks for sleep stage classification. In: Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence IJCAI-PRICAI-20","DOI":"10.24963\/ijcai.2020\/184"},{"key":"151_CR17","doi-asserted-by":"crossref","unstructured":"Zhou K, Song Q, Huang X, Zha D, Hu X (2020) Multi-channel graph neural networks. In: Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence IJCAI-PRICAI-20","DOI":"10.24963\/ijcai.2020\/188"},{"key":"151_CR18","unstructured":"Bruna J, Zaremba W, Szlam A, Lecun Y (2013) Spectral networks and locally connected networks on graphs. Comput Sci"},{"key":"151_CR19","doi-asserted-by":"publisher","unstructured":"Halta\u015f K, Erguzen A, Erdal E (2019) Classification methods in eeg based motor imagery bci systems. 1\u20135. https:\/\/doi.org\/10.1109\/ISMSIT.2019.8932947","DOI":"10.1109\/ISMSIT.2019.8932947"},{"key":"151_CR20","doi-asserted-by":"crossref","unstructured":"Lu N, Li T, Ren X, Miao H (2016) A deep learning scheme for motor imagery classification based on restricted boltzmann machines. IEEE Trans Neural Syst Rehabil Eng","DOI":"10.1109\/TNSRE.2016.2601240"},{"key":"151_CR21","doi-asserted-by":"crossref","unstructured":"Aggarwal S, Chugh N (2017) Signal processing techniques for motor imagery brain computer interface: a review. Array 1\u20132","DOI":"10.1016\/j.array.2019.100003"},{"key":"151_CR22","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1609\/aaai.v33i01.3301922","volume":"33","author":"S Guo","year":"2019","unstructured":"Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc AAAI Conf Artif Intell 33:922\u2013929. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301922","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"151_CR23","doi-asserted-by":"crossref","unstructured":"Li C, Cui Z, Zheng W, Xu C, Yang J (2018) Spatio-temporal graph convolution for skeleton based action recognition","DOI":"10.1109\/ITSC.2019.8916929"},{"key":"151_CR24","first-page":"3656","volume":"33","author":"X Geng","year":"2019","unstructured":"Geng X, Li Y, Wang L, Zhang L, Liu Y (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. Proc AAAI Conf Artif Intell 33:3656\u20133663","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"3","key":"151_CR25","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1175\/1520-0493(2001)129<0540:EDAASM>2.0.CO;2","volume":"129","author":"KL Elmore","year":"2010","unstructured":"Elmore KL, Richman MB (2010) Euclidean distance as a similarity metric for principal component analysis. Monthly Weather Rev 129(3):540\u2013549","journal-title":"Monthly Weather Rev"},{"issue":"6","key":"151_CR26","doi-asserted-by":"publisher","first-page":"2611","DOI":"10.1109\/TIT.2010.2046212","volume":"56","author":"T Klve","year":"2010","unstructured":"Klve T, Lin TT, Tsai SC, Tzeng WG (2010) Permutation arrays under the Chebyshev distance. IEEE Trans Inform Theor 56(6):2611\u20132617","journal-title":"IEEE Trans Inform Theor"},{"key":"151_CR27","unstructured":"Dongen SV, Enright AJ (2012) Metric distances derived from cosine similarity and Pearson and Spearman correlations. Comput Ence"},{"key":"151_CR28","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1093\/biomet\/70.1.163","volume":"1","author":"JT Kent","year":"1983","unstructured":"Kent JT (1983) Information gain and a general measure of correlation. Biometrika 1:163\u2013173","journal-title":"Biometrika"},{"issue":"2","key":"151_CR29","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1109\/42.563664","volume":"16","author":"F Maes","year":"1997","unstructured":"Maes F, Collignon A (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imag 16(2):187\u2013198","journal-title":"IEEE Trans Med Imag"},{"key":"151_CR30","unstructured":"Li Y, Zhong N, Taniar D, Zhang H (2021) Mutualgraphnet: a novel model for motor imagery classification. arxiv preprint arxiv:2109.04361"},{"key":"151_CR31","doi-asserted-by":"crossref","unstructured":"Feng X, Jiang G, Bing Q, Liu T, Liu Y (2017) Effective deep memory networks for distant supervised relation extraction. In: Twenty-Sixth International Joint Conference on Artificial Intelligence","DOI":"10.24963\/ijcai.2017\/559"},{"key":"151_CR32","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering"},{"key":"151_CR33","doi-asserted-by":"crossref","unstructured":"Chang CC, Lin CJ (2007) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3, article 27)","DOI":"10.1145\/1961189.1961199"},{"key":"151_CR34","unstructured":"Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 23(23)"},{"key":"151_CR35","unstructured":"Ishida T, Yamane I, Sakai T, Niu G, Sugiyama M (2020) Do we need zero training loss after achieving zero training error?"},{"key":"151_CR36","doi-asserted-by":"crossref","unstructured":"Mugruza-Vassallo CA, Potter DD, Tsiora S, Macfarlane JA, Maxwell A (2021) Prior context influences motor brain areas in an auditory oddball task and prefrontal cortex multitasking modelling. Brain Informat 8","DOI":"10.1186\/s40708-021-00124-6"},{"key":"151_CR37","unstructured":"Zheng WL, Zhu JY, Lu BL (2016) Identifying stable patterns over time for emotion recognition from eeg"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-021-00151-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-021-00151-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-021-00151-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T12:00:39Z","timestamp":1643716839000},"score":1,"resource":{"primary":{"URL":"https:\/\/braininformatics.springeropen.com\/articles\/10.1186\/s40708-021-00151-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,1]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["151"],"URL":"https:\/\/doi.org\/10.1186\/s40708-021-00151-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1014504\/v1","asserted-by":"object"}]},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,1]]},"assertion":[{"value":"2 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3"}}