{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:09:02Z","timestamp":1753891742914,"version":"3.41.2"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Motor imagery electroencephalographic (MI-EEG) signal recognition is used in various brain\u2013computer interface (BCI) systems. In most existing BCI systems, this identification relies on classification algorithms. However, generally, a large amount of subject-specific labeled training data is required to reliably calibrate the classification algorithm for each new subject. To address this challenge, an effective strategy is to integrate transfer learning into the construction of intelligent models, allowing knowledge to be transferred from the source domain to enhance the performance of models trained in the target domain. Although transfer learning has been implemented in EEG signal recognition, many existing methods are designed specifically for certain intelligent models, limiting their application and generalization.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To broaden application and generalization, an extended-LSR-based inductive transfer learning method is proposed to facilitate transfer learning across various classical intelligent models, including neural networks, Takagi-SugenoKang (TSK) fuzzy systems, and kernel methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results and discussion<\/jats:title><jats:p>The proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. The experimental results demonstrate the effectiveness of the proposed method in MI-EEG signal recognition.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fninf.2025.1559335","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T05:22:29Z","timestamp":1744176149000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning"],"prefix":"10.3389","volume":"19","author":[{"given":"Zhibin","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keli","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Qu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zekang","family":"Bian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghua","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1038\/s43588-021-00084-1","article-title":"The power of quantum neural networks","volume":"1","author":"Abbas","year":"2021","journal-title":"Nat. Comput. Sci."},{"key":"ref2","first-page":"115","article-title":"Classifications of motor imagery tasks using k-nearest neighbors","author":"Aldea","year":"2014","journal-title":"In 12th symposium on Neural Network applications in electrical engineering (NEUREL)"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1109\/83.136597","article-title":"Image coding using wavelet transform","volume":"1","author":"Antonini","year":"1992","journal-title":"IEEE Trans. Image Process."},{"key":"ref4","first-page":"368","article-title":"Semi-supervised support vector machines","volume":"11","author":"Bennett","year":"1999","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"102099","DOI":"10.1016\/j.inffus.2023.102099","article-title":"Weighted adaptively ensemble clustering method based on fuzzy co-association matrix","volume":"103","author":"Bian","year":"2024","journal-title":"Inform. Fusion"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/51.566156","article-title":"Applying time-frequency analysis to seizure EEG activity","volume":"16","author":"Blanco","year":"1997","journal-title":"Engin. Med. Biol. Magazine IEEE"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TNSRE.2006.875642","article-title":"The BCI competition III: validating alternative approaches to actual BCI problems","volume":"14","author":"Blankertz","year":"2006","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref9","first-page":"193","article-title":"Boosting for transfer learning","author":"Dai","year":"2007"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/RBME.2024.3449790","article-title":"Non-invasive brain-computer interfaces: state of the art and trends","volume":"18","author":"Edelman","year":"2024","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugenics"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13721-020-00268-1","article-title":"Improving the performance of P300 BCI system using different methods","volume":"9","author":"Fouad","year":"2020","journal-title":"Network Model. Analysis Health Inform. Bioinform."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s40860-020-00117-y","article-title":"Optimization of parameters for improving the performance of EEG-based BCI system","volume":"7","author":"Ghumman","year":"2021","journal-title":"J. Reliable Intelligent Environ."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1016\/j.clinph.2008.02.001","article-title":"A comparison of quantitative EEG features for neonatal seizure detection","volume":"119","author":"Greene","year":"2008","journal-title":"Clin. Neurophysiol."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/TETCI.2023.3336537","article-title":"Takagi\u2013sugeno\u2013Kang fuzzy clustering by direct fuzzy inference on fuzzy rules","volume":"8","author":"Gu","year":"2024","journal-title":"IEEE Transact. Emerg. Topics Comput. Intelligence"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/TNN.2006.875977","article-title":"Universal approximation using incremental constructive feedforward networks with random hidden nodes","volume":"17","author":"Huang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TNSRE.2019.2904708","article-title":"Recognition of multiclass epileptic EEG signals based on knowledge and label space inductive transfer","volume":"27","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/MCI.2021.3061875","article-title":"Multi-scale neural network for EEG representation learning in BCI","volume":"16","author":"Ko","year":"2021","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref19","first-page":"202","article-title":"Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid","volume":"96","author":"Kohavi","year":"1996","journal-title":"KDD"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"066050","DOI":"10.1088\/1741-2552\/ac42b4","article-title":"Feature selection method based on Menger curvature and LDA theory for a P300 brain\u2013computer interface","volume":"18","author":"Li","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"3281","DOI":"10.1109\/TCYB.2019.2904052","article-title":"Multisource transfer learning for cross-subject EEG emotion recognition","volume":"50","author":"Li","year":"2019","journal-title":"IEEE Transact. Cybernet."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/TBME.2010.2082539","article-title":"Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms","volume":"58","author":"Lotte","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"40","DOI":"10.4103\/jmss.JMSS_74_20","article-title":"Electroencephalography-based brain\u2013computer interface motor imagery classification","volume":"12","author":"Mohammadi","year":"2022","journal-title":"J. Med. Signals Sensors"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1109\/TPAMI.2010.128","article-title":"Linear regression for face recognition","volume":"32","author":"Naseem","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref25","doi-asserted-by":"publisher","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":"ref26","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/86.712230","article-title":"Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters","volume":"6","author":"Pfurtscheller","year":"1998","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"1179414","DOI":"10.3389\/fmicb.2023.1179414","article-title":"A new integrated framework for the identification of potential virus\u2013drug associations","volume":"14","author":"Qu","year":"","journal-title":"Front. Microbiol."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"e15889","DOI":"10.7717\/peerj.15889","article-title":"Neighborhood-based inference and restricted Boltzmann machine for small molecule-miRNA associations prediction","volume":"11","author":"Qu","year":"","journal-title":"PeerJ"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"20676","DOI":"10.1109\/JSEN.2022.3204121","article-title":"Spectral image-based multiday surface electromyography classification of hand motions using CNN for human\u2013computer interaction","volume":"22","author":"Qureshi","year":"2022","journal-title":"IEEE Sensors J."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"8989","DOI":"10.1109\/JSEN.2023.3255408","article-title":"E2cnn: an efficient concatenated cnn for classification of surface emg extracted from upper limb","volume":"23","author":"Qureshi","year":"2023","journal-title":"IEEE Sensors J."},{"key":"ref32","first-page":"515","article-title":"Ridge regression learning algorithm in dual variables","author":"Saunders","year":"1998"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1109\/TAU.1973.1162474","article-title":"Design and simulation of a speech analysis-synthesis system based on short-time Fourier analysis","volume":"21","author":"Schafer","year":"1973","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.32604\/cmc.2023.041970","article-title":"Electroencephalography (EEG) based neonatal sleep staging and detection using various classification algorithms","volume":"77","author":"Siddiqa","year":"2023","journal-title":"Comput. Materials Continua"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"29910","DOI":"10.1109\/ACCESS.2024.3365570","article-title":"Single-Channel EEG data analysis using a multi-branch CNN for neonatal sleep staging","volume":"12","author":"Siddiqa","year":"2024","journal-title":"IEEE Access"},{"key":"ref36","doi-asserted-by":"publisher","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":"ref37","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.measurement.2016.02.059","article-title":"Detection of motor imagery EEG signals employing Na\u00efve Bayes based learning process","volume":"86","author":"Wang","year":"2016","journal-title":"Measurement"},{"key":"ref38","doi-asserted-by":"crossref","DOI":"10.1145\/1015330.1015436","article-title":"Improving SVM accuracy by training on auxiliary data sources","author":"Wu","year":"2004"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.1109\/TCYB.2018.2821764","article-title":"Generalized hidden-mapping transductive transfer learning for recognition of epileptic electroencephalogram signals","volume":"49","author":"Xie","year":"2018","journal-title":"IEEE Transact. Cybernet."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"1205529","DOI":"10.3389\/fninf.2023.1205529","article-title":"Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition","volume":"17","author":"Zhang","year":"2023","journal-title":"Front. Neuroinform."},{"key":"ref41","doi-asserted-by":"crossref","first-page":"120976","DOI":"10.1016\/j.ins.2024.120976","article-title":"A dynamic broad TSK fuzzy classifier based on iterative learning on progressively rebalanced data","volume":"677","author":"Zhang","year":"2024","journal-title":"Inf. Sci."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.inffus.2022.12.014","article-title":"TSK fuzzy system fusion at sensitivity-ensemble-level for imbalanced data classification","volume":"92","author":"Zhang","year":"2023","journal-title":"Inform. Fusion"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"2387","DOI":"10.1109\/TCBB.2022.3142748","article-title":"Multi-modality Fusion & Inductive Knowledge Transfer Underlying non-Sparse Multi-Kernel Learning and distribution adaption","volume":"20","author":"Zhang","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref44","first-page":"3713","article-title":"Be your own teacher: Improve the performance of convolutional neural networks via self distillation","author":"Zhang","year":"2019"}],"container-title":["Frontiers in Neuroinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2025.1559335\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T05:22:30Z","timestamp":1744176150000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2025.1559335\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,9]]},"references-count":44,"alternative-id":["10.3389\/fninf.2025.1559335"],"URL":"https:\/\/doi.org\/10.3389\/fninf.2025.1559335","relation":{},"ISSN":["1662-5196"],"issn-type":[{"type":"electronic","value":"1662-5196"}],"subject":[],"published":{"date-parts":[[2025,4,9]]},"article-number":"1559335"}}