{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:51:58Z","timestamp":1740149518905,"version":"3.37.3"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T00:00:00Z","timestamp":1647216000000},"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":["61603223"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013088","name":"Jiangsu Provincial Qinglan Project","doi-asserted-by":"crossref","award":["2021"],"id":[{"id":"10.13039\/501100013088","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Suzhou Science and Technology Programme","award":["SYG202106"]},{"name":"Research Development Fund of XJTLU","award":["RDF-18-02-30","RDF-20-01-18"]},{"name":"Key Program Special Fund in XJTLU","award":["KSF-E-34"]},{"name":"The Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["20KJB520034"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Brain\u2013computer interface (BCI) research has attracted worldwide attention and has been rapidly developed. As one well-known non-invasive BCI technique, electroencephalography (EEG) records the brain\u2019s electrical signals from the scalp surface area. However, due to the non-stationary nature of the EEG signal, the distribution of the data collected at different times or from different subjects may be different. These problems affect the performance of the BCI system and limit the scope of its practical application. In this study, an unsupervised deep-transfer-learning-based method was proposed to deal with the current limitations of BCI systems by applying the idea of transfer learning to the classification of motor imagery EEG signals. The Euclidean space data alignment (EA) approach was adopted to align the covariance matrix of source and target domain EEG data in Euclidean space. Then, the common spatial pattern (CSP) was used to extract features from the aligned data matrix, and the deep convolutional neural network (CNN) was applied for EEG classification. The effectiveness of the proposed method has been verified through the experiment results based on public EEG datasets by comparing with the other four methods.<\/jats:p>","DOI":"10.3390\/s22062241","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T06:56:20Z","timestamp":1647327380000},"page":"2241","source":"Crossref","is-referenced-by-count":18,"title":["An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain\u2013Computer Interface"],"prefix":"10.3390","volume":"22","author":[{"given":"Xuying","family":"Wang","sequence":"first","affiliation":[{"name":"School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"},{"name":"School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, Liverpool L69 3BX, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5634-5476","authenticated-orcid":false,"given":"Rui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"},{"name":"Research Institute of Big Data Analytics, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8163-8679","authenticated-orcid":false,"given":"Mengjie","family":"Huang","sequence":"additional","affiliation":[{"name":"Design School, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.neucom.2015.02.057","article-title":"Multi-objective genetic algorithm as channel selection method for P300 and motor imagery data set","volume":"161","author":"Kee","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1146\/annurev.bb.02.060173.001105","article-title":"Toward direct brain-computer communication","volume":"2","author":"Vidal","year":"1973","journal-title":"Annu. Rev. Biophys. Bioeng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"E25","DOI":"10.3171\/2010.2.FOCUS1027","article-title":"Brain-computer interfaces: Military, neurosurgical, and ethical perspective","volume":"28","author":"Kotchetkov","year":"2010","journal-title":"Neurosurg. Focus"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16010","DOI":"10.1016\/j.ifacol.2020.12.399","article-title":"Design of a hybrid brain-computer interface and virtual reality system for post-stroke rehabilitation","volume":"53","author":"Huang","year":"2020","journal-title":"IFAC-PapersOnLine"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, X., Yang, R., Huang, M., Yang, Z., and Wan, Z. (2021, January 9\u201311). A hybrid transfer learning approach for motor imagery classification in brain-computer interface. Proceedings of the 2021 IEEE 3rd Global Conference on Life Sciences and Technologies, Nara, Japan.","DOI":"10.1109\/LifeTech52111.2021.9391933"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wei, M., Yang, R., and Huang, M. (2021, January 7\u20139). Motor imagery EEG signal classification based on deep transfer learning. Proceedings of the 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, Aveiro, Portugal.","DOI":"10.1109\/CBMS52027.2021.00083"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.neucom.2021.11.039","article-title":"EEG fading data classification based on improved manifold learning with adaptive neighborhood selection","volume":"482","author":"Wan","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S1388-2457(02)00411-X","article-title":"A brain\u2013computer interface (BCI) for the locked-in: Comparison of different EEG classifications for the thought translation device","volume":"114","author":"Hinterberger","year":"2003","journal-title":"Clin. Neurophysiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"241","DOI":"10.12700\/APH.17.9.2020.9.13","article-title":"Study of Algorithmic Problem-Solving and Executive Function","volume":"17","author":"Kovari","year":"2020","journal-title":"Acta Polytech. Hung."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.12700\/APH.17.2.2020.2.4","article-title":"Evaluation of eye-movement metrics in a software debbuging task using gp3 eye tracker","volume":"17","author":"Kovari","year":"2020","journal-title":"Acta Polytech. Hung."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.12700\/APH.17.2.2020.2.5","article-title":"Quantitative analysis of relationship between visual attention and eye-hand coordination","volume":"17","author":"Kovari","year":"2020","journal-title":"Acta Polytech. Hung"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kovari, A., Katona, J., Heldal, I., Helgesen, C., Costescu, C., Rosan, A., Hathazi, A., Thill, S., and Demeter, R. (2019, January 23\u201325). Examination of gaze fixations recorded during the trail making test. Proceedings of the 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Naples, Italy.","DOI":"10.1109\/CogInfoCom47531.2019.9089937"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Katona, J., Ujbanyi, T., Sziladi, G., and Kovari, A. (2017, January 11\u201314). Examine the effect of different web-based media on human brain waves. Proceedings of the 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Debrecen, Hungary.","DOI":"10.1109\/CogInfoCom.2017.8268280"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Costescu, C., Rosan, A., Brigitta, N., Hathazi, A., Kovari, A., Katona, J., Demeter, R., Heldal, I., Helgesen, C., and Thill, S. (2019, January 23\u201325). Assessing Visual Attention in Children Using GP3 Eye Tracker. Proceedings of the 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Naples, Italy.","DOI":"10.1109\/CogInfoCom47531.2019.9089995"},{"key":"ref_15","first-page":"32","article-title":"Application of Eye Movement Monitoring Technique in Teaching Process","volume":"17","author":"Pinter","year":"2021","journal-title":"IPSI Trans. Adv. Res."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1109\/TBME.2004.827072","article-title":"BCI2000: A general-purpose brain-computer interface (BCI) system","volume":"51","author":"Schalk","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/S1388-2457(98)00038-8","article-title":"Designing optimal spatial filters for single-trial EEG classification in a movement task","volume":"110","author":"Pfurtscheller","year":"1999","journal-title":"Clin. Neurophysiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"R32","DOI":"10.1088\/1741-2560\/4\/2\/R03","article-title":"A survey of signal processing algorithms in brain\u2013computer interfaces based on electrical brain signals","volume":"4","author":"Bashashati","year":"2007","journal-title":"J. Neural Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"573643","DOI":"10.1155\/2015\/573643","article-title":"Cardiovascular system changes and related risk factors in acromegaly patients: A case-control study","volume":"2015","author":"Guo","year":"2015","journal-title":"Int. J. Endocrinol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/TBME.2002.803536","article-title":"Design and implementation of a brain-computer interface with high transfer rates","volume":"49","author":"Cheng","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1109\/86.847821","article-title":"Current trends in Graz brain-computer interface (BCI) research","volume":"8","author":"Pfurtscheller","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"056001","DOI":"10.1088\/1741-2560\/8\/5\/056001","article-title":"EEG potentials predict upcoming emergency brakings during simulated driving","volume":"8","author":"Haufe","year":"2011","journal-title":"J. Neural Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2020.09.017","article-title":"A review on transfer learning in EEG signal analysis","volume":"421","author":"Wan","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1109\/TBME.2019.2913914","article-title":"Transfer learning for Brain\u2013Computer interfaces: A Euclidean space data alignment approach","volume":"67","author":"He","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/BF01129656","article-title":"Spatial patterns underlying population differences in the background EEG","volume":"2","author":"Koles","year":"1990","journal-title":"Brain Topogr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/TASLP.2014.2339736","article-title":"Convolutional neural networks for speech recognition","volume":"22","author":"Mohamed","year":"2014","journal-title":"IEEE\/ACM Trans. Audio Speech, Lang. Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_33","unstructured":"BCI-Horizon (2022, February 01). BCI Dataset. Available online: http:\/\/www.bnci-horizon-2020.eu\/database\/data-sets."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1109\/5.939829","article-title":"Motor imagery and direct brain-computer communication","volume":"89","author":"Pfurtscheller","year":"2001","journal-title":"Proc. IEEE"},{"key":"ref_35","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2241\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T16:11:14Z","timestamp":1736266274000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,14]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062241"],"URL":"https:\/\/doi.org\/10.3390\/s22062241","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,3,14]]}}}