{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T07:42:26Z","timestamp":1768722146886,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:00:00Z","timestamp":1661904000000},"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":["61471140"],"award-info":[{"award-number":["61471140"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["IR2021222"],"award-info":[{"award-number":["IR2021222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2016RALGJ001"],"award-info":[{"award-number":["2016RALGJ001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["216506"],"award-info":[{"award-number":["216506"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["61471140"],"award-info":[{"award-number":["61471140"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["IR2021222"],"award-info":[{"award-number":["IR2021222"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2016RALGJ001"],"award-info":[{"award-number":["2016RALGJ001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["216506"],"award-info":[{"award-number":["216506"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sci-tech Innovation Foundation of Harbin","award":["61471140"],"award-info":[{"award-number":["61471140"]}]},{"name":"Sci-tech Innovation Foundation of Harbin","award":["IR2021222"],"award-info":[{"award-number":["IR2021222"]}]},{"name":"Sci-tech Innovation Foundation of Harbin","award":["2016RALGJ001"],"award-info":[{"award-number":["2016RALGJ001"]}]},{"name":"Sci-tech Innovation Foundation of Harbin","award":["216506"],"award-info":[{"award-number":["216506"]}]},{"name":"China Scholarship Council and the Future Science and Technology Innovation Team Project of HIT","award":["61471140"],"award-info":[{"award-number":["61471140"]}]},{"name":"China Scholarship Council and the Future Science and Technology Innovation Team Project of HIT","award":["IR2021222"],"award-info":[{"award-number":["IR2021222"]}]},{"name":"China Scholarship Council and the Future Science and Technology Innovation Team Project of HIT","award":["2016RALGJ001"],"award-info":[{"award-number":["2016RALGJ001"]}]},{"name":"China Scholarship Council and the Future Science and Technology Innovation Team Project of HIT","award":["216506"],"award-info":[{"award-number":["216506"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A brain-computer interface (BCI) translates a user\u2019s thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of BCI illiterate subjects are low classification rates and poor repeatability. To address the problem of MI-BCI illiteracy, we propose a distribution adaptation method based on multi-kernel learning to make the distribution of features between the source domain and target domain become even closer to each other, while the divisibility of categories is maximized. Inspired by the kernel trick, we adopted a multiple-kernel-based extreme learning machine to train the labeled source-domain data to find a new high-dimensional subspace that maximizes data divisibility, and then use multiple-kernel-based maximum mean discrepancy to conduct distribution adaptation to eliminate the difference in feature distribution between domains in the new subspace. In light of the high dimension of features of MI-BCI illiteracy, random forest, which can effectively handle high-dimensional features without additional cross-validation, was employed as a classifier. The proposed method was validated on an open dataset. The experimental results show that that the method we proposed suits MI-BCI illiteracy and can reduce the inter-domain differences, resulting in a reduction in the performance degradation of both cross-subjects and cross-sessions.<\/jats:p>","DOI":"10.3390\/s22176572","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T03:55:38Z","timestamp":1662004538000},"page":"6572","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Distribution Adaptation and Classification Framework Based on Multiple Kernel Learning for Motor Imagery BCI Illiteracy"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7230-0921","authenticated-orcid":false,"given":"Lin","family":"Tao","sequence":"first","affiliation":[{"name":"School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8797-0709","authenticated-orcid":false,"given":"Tianao","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4974-0275","authenticated-orcid":false,"given":"Qisong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinwei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3109\/17483107.2011.589486","article-title":"Toward functioning and usable brain-computer interfaces (BCIs): A literature review","volume":"7","author":"Pasqualotto","year":"2012","journal-title":"Disabil. Rehabil. Assist. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Blankertz, B., and Vidaurre, C. (2009). Towards a cure for BCI illiteracy: Machine learning based co-adaptive learning. BMC Neurosci., 10.","DOI":"10.1186\/1471-2202-10-S1-P85"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1016\/j.neuroimage.2010.03.022","article-title":"Neurophysiological predictor of SMR-based BCI performance","volume":"51","author":"Blankertz","year":"2010","journal-title":"Neuroimage"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Kragel, P.A., Knodt, A.R., Hariri, A.R., and Labar, K.S. (2016). Decoding Spontaneous Emotional States in the Human Brain. PLoS Biol., 14.","DOI":"10.1371\/journal.pbio.2000106"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Weiss, K., Khoshgoftaar, T.M., and Wang, D.D. (2016). A Survey of Transfer Learning, Springer International Publishing.","DOI":"10.1186\/s40537-016-0043-6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"585","DOI":"10.3389\/fnhum.2017.00585","article-title":"Enhanced motor imagery-based BCI performance via tactile stimulation on unilateral hand","volume":"11","author":"Shu","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","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":"Lee","year":"2019","journal-title":"Gigascience"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, Y., Lan, Z., Cui, J., Sourina, O., and Muller-Wittig, W. (2019, January 2\u20134). EEG-Based cross-subject mental fatigue recognition. Proceedings of the 2019 International Conference on Cyberworlds (CW), Kyoto, Japan.","DOI":"10.1109\/CW.2019.00048"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1007\/s10111-017-0425-3","article-title":"Cross-subject mental workload classification using kernel spectral regression and transfer learning techniques","volume":"19","author":"Zhang","year":"2017","journal-title":"Cogn. Technol. Work"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chai, X., Wang, Q., Zhao, Y., Li, Y., Liu, D., Liu, X., and Bai, O. (2017). A fast, efficient domain adaptation technique for cross-domain electroencephalography(EEG)-based emotion recognition. Sensors, 17.","DOI":"10.3390\/s17051014"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1109\/TNSRE.2020.2980299","article-title":"Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach","volume":"28","author":"He","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hua, Y., Zhong, X., Zhang, B., Yin, Z., and Zhang, J. (2021). Manifold feature fusion with dynamical feature selection for cross-subject emotion recognition. Brain Sci., 11.","DOI":"10.3390\/brainsci11111392"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1109\/JSTARS.2021.3049527","article-title":"Combining Multiple Classifiers for Domain Adaptation of Remote Sensing Image Classification","volume":"14","author":"Wei","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1109\/TGRS.2018.2872850","article-title":"Centroid and Covariance Alignment-Based Domain Adaptation for Unsupervised Classification of Remote Sensing Images","volume":"57","author":"Ma","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","first-page":"1303","article-title":"Multiple Kernel Learning Algorithms","volume":"42","author":"Liu","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1109\/TPAMI.2013.212","article-title":"Multiple kernel learning for visual object recognition: A review","volume":"36","author":"Bucak","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","unstructured":"Long, M., Cao, Y., Wang, J., and Jordan, M.I. (2015, January 7\u20139). Learning transferable features with deep adaptation networks. Proceedings of the 32nd International Conference on Machine Learning, ICML, Lille, France."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1109\/LGRS.2012.2236818","article-title":"Learn multiple-kernel SVMs for domain adaptation in hyperspectral data","volume":"10","author":"Sun","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"49951","DOI":"10.1109\/ACCESS.2019.2908851","article-title":"Domain Transfer Multiple Kernel Boosting for Classification of EEG Motor Imagery Signals","volume":"7","author":"Dai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.patcog.2017.10.007","article-title":"Active multi-kernel domain adaptation for hyperspectral image classification","volume":"77","author":"Deng","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2292","DOI":"10.1109\/TMM.2019.2900166","article-title":"Multi-Kernel Coupled Projections for Domain Adaptive Dictionary Learning","volume":"21","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.patcog.2018.07.035","article-title":"Semi-supervised domain adaptation via Fredholm integral based kernel methods","volume":"85","author":"Wang","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, X., and Lengel\u013ae, \u0154. (2017, January 24\u201326). Domain adaptation transfer learning by SVM subject to a maximum-mean-discrepancy-like constraint. Proceedings of the ICPRAM 2017\u20146th International Conference on Pattern Recognition Applications and Methods, Porto, Portugal.","DOI":"10.5220\/0006119900890095"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, M., Huang, Y., and Nehorai, A. (2018, January 18\u201323). Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00362"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.neucom.2013.09.072","article-title":"Multiple kernel extreme learning machine","volume":"149","author":"Liu","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain adaptation via transfer component analysis","volume":"22","author":"Pan","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_28","first-page":"1299","article-title":"Nonlinear Component Analysis as a Kernel Eigenvalue Problem","volume":"1319","author":"Sch","year":"1998","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., and Yu, P.S. (2013, January 1\u20138). Transfer feature learning with joint distribution adaptation. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1109\/TNSRE.2022.3157959","article-title":"Feature and Classification Analysis for Detection and Classification of Tongue Movements from Single-Trial Pre-Movement EEG","volume":"30","author":"Kaeseler","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"199719","DOI":"10.1109\/ACCESS.2020.3035539","article-title":"Emotion Recognition Related to Stock Trading Using Machine Learning Algorithms with Feature Selection","volume":"8","author":"Torres","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bentlemsan, M., Zemouri, E.T., Bouchaffra, D., Yahya-Zoubir, B., and Ferroudji, K. (2015, January 9\u201312). Random forest and filter bank common spatial patterns for EEG-based motor imagery classification. Proceedings of the International Conference on Intelligent Systems, Modelling and Simulation, ISMS, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ISMS.2014.46"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.neucom.2010.02.019","article-title":"Optimization method based extreme learning machine for classification","volume":"74","author":"Huang","year":"2010","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.ces.2003.09.012","article-title":"Nonlinear process monitoring using kernel principal component analysis","volume":"59","author":"Lee","year":"2004","journal-title":"Chem. Eng. Sci."},{"key":"ref_36","first-page":"1063","article-title":"Analysis of a random forests model","volume":"13","author":"Biau","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JEI.27.6.063029","article-title":"Iterative landmark selection and subspace alignment for unsupervised domain adaptation","volume":"27","author":"Xiao","year":"2018","journal-title":"J. Electron. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wei, J. (2018, January 23\u201327). Learning Discriminative Geodesic Flowkernel For Unsupervised Domain Adaption. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA.","DOI":"10.1109\/ICME.2018.8486446"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MCI.2015.2501545","article-title":"Transfer Learning in Brain-Computer Interfaces","volume":"11","author":"Jayaram","year":"2016","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/1741-2560\/4\/2\/R01","article-title":"A review of classification algorithms for EEG-based brain-computer interfaces","volume":"4","author":"Lotte","year":"2007","journal-title":"J. Neural Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_42","unstructured":"Mane, R., Chew, E., Chua, K., Ang, K.K., Robinson, N., Vinod, A.P., Lee, S.-W., and Guan, C. (2021). FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Giannakakis, G., Trivizakis, E., Tsiknakis, M., and Marias, K. (2019, January 3\u20136). A novel multi-kernel 1D convolutional neural network for stress recognition from ECG. Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW, Cambridge, UK.","DOI":"10.1109\/ACIIW.2019.8925020"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6572\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:21:01Z","timestamp":1760142061000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6572"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,31]]},"references-count":43,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176572"],"URL":"https:\/\/doi.org\/10.3390\/s22176572","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,31]]}}}