{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T10:17:05Z","timestamp":1788171425783,"version":"build-2803163510"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s13042-026-03247-6","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T05:38:34Z","timestamp":1785130714000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Source-free adaptation for cross-domain EEG signal recognition"],"prefix":"10.1007","volume":"17","author":[{"given":"Zhuoyuan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anqi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianqi","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"3247_CR1","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.1038\/s41598-025-86040-9","volume":"15","author":"M Roshanaei","year":"2025","unstructured":"Roshanaei M, Norouzi H, Onton J, Makeig S, Mohammadi A (2025) Eeg-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis. Sci Rep 15:2174","journal-title":"Sci Rep"},{"key":"3247_CR2","doi-asserted-by":"crossref","unstructured":"Zabcikova M, Koudelkova Z, Jasek R (2022) EEG-based lie detection using ERP P300 in response to known and unknown faces: an overview. In: 26th international conference on circuits, systems, communications and computers vol 94, no. 1, pp 11\u201315","DOI":"10.1109\/CSCC55931.2022.00011"},{"key":"3247_CR3","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1109\/LSP.2022.3179946","volume":"29","author":"Y Gao","year":"2022","unstructured":"Gao Y, Fu X, Ouyang T et al (2022) EEG-GCN: Spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition. IEEE Signal Process Lett 29:1574\u20131578","journal-title":"IEEE Signal Process Lett"},{"key":"3247_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110261","volume":"150","author":"A Dutta","year":"2024","unstructured":"Dutta A, Biswas S, Das AK (2024) Emocomicnet: a multi-task model for comic emotion recognition. Pattern Recogn 150:110261","journal-title":"Pattern Recogn"},{"key":"3247_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126262","volume":"544","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Li F, Li Y, Ji Y, Shi G, Zheng W, Zhang L, Chen Y, Cheng R (2023) Progressive graph convolution network for eeg emotion recognition. Neurocomputing 544:126262","journal-title":"Neurocomputing"},{"issue":"5","key":"3247_CR6","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern VJ, Solon AJ, Waytowich NR et al (2018) EEGnet: a compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng 15(5):056013","journal-title":"J Neural Eng"},{"issue":"5","key":"3247_CR7","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1109\/TPAMI.2018.2832198","volume":"41","author":"J Liang","year":"2018","unstructured":"Liang J, He R, Sun Z et al (2018) Aggregating randomized clustering-promoting invariant projections for domain adaptation. IEEE Trans Pattern Anal Mach Intell 41(5):1027\u20131042","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3247_CR8","first-page":"97","volume":"99","author":"M Long","year":"2015","unstructured":"Long M, Cao Y, Wang J et al (2015) Learning transferable features with deep adaptation networks. Int Confe Mach Learn 99:97\u2013105","journal-title":"Int Confe Mach Learn"},{"key":"3247_CR9","doi-asserted-by":"crossref","unstructured":"Hjerppe K, Ruohonen J, Lepp\u00e4nen V (2019) The general data protection regulation: requirements, architectures, and constraints. In: IEEE 27th international requirements engineering conference. pp 265\u2013275","DOI":"10.1109\/RE.2019.00036"},{"key":"3247_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126921","volume":"564","author":"N Zhang","year":"2024","unstructured":"Zhang N, Lu J, Li K, Fang Z, Zhang G (2024) Source-free unsupervised domain adaptation: current research and future directions. Neurocomputing 564:126921","journal-title":"Neurocomputing"},{"key":"3247_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109974","volume":"145","author":"L Zhou","year":"2024","unstructured":"Zhou L, Li N, Ye M, Zhu X, Tang S (2024) Source-free domain adaptation with class prototype discovery. Pattern Recogn 145:109974","journal-title":"Pattern Recogn"},{"issue":"7","key":"3247_CR12","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/5.939829","volume":"89","author":"G Pfurtscheller","year":"2001","unstructured":"Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain-computer communication. Proc IEEE 89(7):1123\u20131134","journal-title":"Proc IEEE"},{"key":"3247_CR13","doi-asserted-by":"crossref","unstructured":"L\u00f3pez PS, Iversen HK, Puthusserypady S (2019) An efficient multi-class mi based bci scheme using statistical fusion techniques of classifiers. In: TENCON 2019-2019 IEEE Region 10 Conference, pp. 378\u2013382","DOI":"10.1109\/TENCON.2019.8929345"},{"key":"3247_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109999","volume":"146","author":"Z Ding","year":"2024","unstructured":"Ding Z, Zhong G, Qin X, Li Q, Fan Z, Deng Z, Ling X, Xiang W (2024) Mf-net: Multi-frequency intrusion detection network for internet traffic data. Pattern Recogn 146:109999","journal-title":"Pattern Recogn"},{"issue":"11","key":"3247_CR15","doi-asserted-by":"publisher","first-page":"6925","DOI":"10.1007\/s00521-018-3531-0","volume":"31","author":"S Taran","year":"2019","unstructured":"Taran S, Bajaj V (2019) Motor imagery tasks-based EEG signals classification using tunable-q wavelet transform. Neural Comput Appl 31(11):6925\u20136932","journal-title":"Neural Comput Appl"},{"key":"3247_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128354","volume":"604","author":"A Apicella","year":"2024","unstructured":"Apicella A, Arpaia P, D\u2019Errico G, Marocco D, Mastrati G, Moccaldi N, Prevete R (2024) Toward cross-subject and cross-session generalization in EEG-based emotion recognition: systematic review, taxonomy, and methods. Neurocomputing 604:128354","journal-title":"Neurocomputing"},{"key":"3247_CR17","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1800\u20131807","DOI":"10.1109\/CVPR.2017.195"},{"key":"3247_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107473","volume":"103","author":"W Tang","year":"2025","unstructured":"Tang W, Fan L, Lin X, Gu Y (2025) EEG emotion recognition based on efficient-capsule network with convolutional attention. Biomed Signal Process Control 103:107473","journal-title":"Biomed Signal Process Control"},{"key":"3247_CR19","doi-asserted-by":"crossref","unstructured":"Xu H, Plataniotis KN (2016) Affective states classification using EEG and semi-supervised deep learning approaches. In: IEEE 18th international workshop on multimedia signal processing. pp 1\u20136","DOI":"10.1109\/MMSP.2016.7813351"},{"key":"3247_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105888","volume":"90","author":"D Devi","year":"2024","unstructured":"Devi D, Sophia S (2024) Ga-cnn: analyzing student\u2019s cognitive skills with EEG data using a hybrid deep learning approach. Biomed Signal Process Control 90:105888","journal-title":"Biomed Signal Process Control"},{"key":"3247_CR21","doi-asserted-by":"crossref","unstructured":"Ding Y, Robinson N, Zeng Q, et al (2020) Tsception: a deep learning framework for emotion detection using EEG. In: International joint conference on neural networks, pp 1\u20137","DOI":"10.1109\/IJCNN48605.2020.9206750"},{"issue":"8","key":"3247_CR22","doi-asserted-by":"publisher","first-page":"5547","DOI":"10.1109\/TII.2021.3133307","volume":"18","author":"P Thuwajit","year":"2021","unstructured":"Thuwajit P, Rangpong P, Sawangjai P et al (2021) EEGWaveNet: Multiscale CNN-based spatiotemporal feature extraction for EEG seizure detection. IEEE Trans Industr Inf 18(8):5547\u20135557","journal-title":"IEEE Trans Industr Inf"},{"key":"3247_CR23","doi-asserted-by":"publisher","first-page":"96984","DOI":"10.1109\/ACCESS.2022.3204758","volume":"10","author":"S Kim","year":"2022","unstructured":"Kim S, Lee D, Lee S (2022) Rethinking CNN architecture for enhancing decoding performance of motor imagery-based EEG signals. IEEE Access 10:96984\u201396996","journal-title":"IEEE Access"},{"issue":"1","key":"3247_CR24","doi-asserted-by":"publisher","first-page":"31319","DOI":"10.1038\/s41598-024-82705-z","volume":"14","author":"Z Cheng","year":"2024","unstructured":"Cheng Z, Bu X, Wang Q, Yang T, Tu J (2024) Eeg-based emotion recognition using multi-scale dynamic cnn and gated transformer. Sci Rep 14(1):31319","journal-title":"Sci Rep"},{"key":"3247_CR25","doi-asserted-by":"crossref","unstructured":"Ding Y, Tong C, Zhang S, Jiang M, Li Y, Lim KJ, Guan C (2025) Emt: a novel transformer for generalized cross-subject EEG emotion recognition. IEEE Trans Neural Netw Learn Syst 8","DOI":"10.1109\/TNNLS.2025.3552603"},{"key":"3247_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainresbull.2024.110901","volume":"208","author":"R Zhang","year":"2024","unstructured":"Zhang R, Guo H, Xu Z, Hu Y, Chen M, Zhang L (2024) Mgfkd: a semi-supervised multi-source domain adaptation algorithm for cross-subject EEG emotion recognition. Brain Res Bull 208:110901","journal-title":"Brain Res Bull"},{"key":"3247_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111137","volume":"283","author":"S Liu","year":"2024","unstructured":"Liu S, Wang Z, An Y, Li B, Wang X, Zhang Y (2024) Da-capsnet: a multi-branch capsule network based on adversarial domain adaption for cross-subject EEG emotion recognition. Knowl-Based Syst 283:111137","journal-title":"Knowl-Based Syst"},{"key":"3247_CR28","first-page":"2208","volume":"70","author":"M Long","year":"2017","unstructured":"Long M, Zhu H, Wang J et al (2017) Deep transfer learning with joint adaptation networks. Int Conf Mach Learn 70:2208\u20132217","journal-title":"Int Conf Mach Learn"},{"issue":"3","key":"3247_CR29","doi-asserted-by":"publisher","first-page":"2077","DOI":"10.1109\/TAFFC.2021.3137857","volume":"14","author":"H Chen","year":"2023","unstructured":"Chen H, Sun S, Li J et al (2023) Personal-zscore: eliminating individual difference for EEG-based cross-subject emotion recognition. IEEE Trans Affect Comput 14(3):2077\u20132088","journal-title":"IEEE Trans Affect Comput"},{"key":"3247_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2020.12.013","volume":"136","author":"K Zhang","year":"2021","unstructured":"Zhang K, Robinson N, Lee S-W et al (2021) Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network. Neural Netw 136:1\u201310","journal-title":"Neural Netw"},{"key":"3247_CR31","doi-asserted-by":"crossref","unstructured":"Duan T, Chauhan M, Shaikh MA, et al (2021) Ultra efficient transfer learning with meta update for cross subject EEG classification","DOI":"10.21428\/594757db.6bc1ca44"},{"key":"3247_CR32","first-page":"173","volume":"453","author":"X Li","year":"2022","unstructured":"Li X, Metsis V (2022) Spp-EEGnet: an input-agnostic self-supervised EEG representation model for inter-dataset transfer learning. Int Conf Comput Inf Technol 453:173\u2013182","journal-title":"Int Conf Comput Inf Technol"},{"key":"3247_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126260","volume":"545","author":"Y Duan","year":"2023","unstructured":"Duan Y, Wang Z, Li Y, Tang J, Wang Y-K, Lin C-T (2023) Cross task neural architecture search for EEG signal recognition. Neurocomputing 545:126260","journal-title":"Neurocomputing"},{"key":"3247_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107337","volume":"102","author":"Y Xiao","year":"2025","unstructured":"Xiao Y, Zhang Y, Peng X, Han S, Zheng X, Fang D, Chen X (2025) Multi-source EEG emotion recognition via dynamic contrastive domain adaptation. Biomed Signal Process Control 102:107337","journal-title":"Biomed Signal Process Control"},{"key":"3247_CR35","doi-asserted-by":"crossref","unstructured":"Lee P, Jeon S, Hwang S, al. (2023) Source-free subject adaptation for EEG-based visual recognition. In: 11th international winter conference on brain-computer interface, pp 1\u20136","DOI":"10.1109\/BCI57258.2023.10078570"},{"issue":"1","key":"3247_CR36","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1109\/TAFFC.2021.3064601","volume":"14","author":"S Song","year":"2023","unstructured":"Song S, Jaiswal S, Sanchez E et al (2023) Self-supervised learning of person-specific facial dynamics for automatic personality recognition. IEEE Trans Affect Comput 14(1):178\u2013195","journal-title":"IEEE Trans Affect Comput"},{"key":"3247_CR37","first-page":"6028","volume":"119","author":"J Liang","year":"2020","unstructured":"Liang J, Hu D, Feng J (2020) Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. Int Conf Mach Learn 119:6028\u20136039","journal-title":"Int Conf Mach Learn"},{"key":"3247_CR38","doi-asserted-by":"crossref","unstructured":"Wang H, Han H, Gan JQ, Wang H (2024) Lightweight source-free domain adaptation based on adaptive Euclidean alignment for brain-computer interfaces. IEEE J Biomed Health Informatics","DOI":"10.1109\/JBHI.2024.3463737"},{"key":"3247_CR39","doi-asserted-by":"crossref","unstructured":"Imtiaz MN, Khan N (2025) Towards practical emotion recognition: an unsupervised source-free approach for EEG domain adaptation.arXiv:2504.03707","DOI":"10.1109\/TAFFC.2026.3713402"},{"issue":"10","key":"3247_CR40","doi-asserted-by":"publisher","first-page":"2128","DOI":"10.1016\/j.clinph.2007.04.019","volume":"118","author":"J Polich","year":"2007","unstructured":"Polich J (2007) Updating P300: an integrative theory of p3a and p3b. Clin Neurophysiol 118(10):2128\u20132148","journal-title":"Clin Neurophysiol"},{"issue":"1","key":"3247_CR41","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.cmpb.2008.10.001","volume":"94","author":"V Abootalebi","year":"2009","unstructured":"Abootalebi V, Moradi MH, Khalilzadeh MA (2009) A new approach for EEG feature extraction in P300-based lie detection. Comput Methods Programs Biomed 94(1):48\u201357","journal-title":"Comput Methods Programs Biomed"},{"key":"3247_CR42","doi-asserted-by":"crossref","unstructured":"Yan H, Ding Y, Li P, et al (2017) Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 945\u2013954","DOI":"10.1109\/CVPR.2017.107"},{"issue":"10","key":"3247_CR43","first-page":"1180","volume":"37","author":"Y Ganin","year":"2015","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. Int Conf Mach Learn 37(10):1180\u20131189","journal-title":"Int Conf Mach Learn"},{"issue":"9","key":"3247_CR44","first-page":"775","volume":"23","author":"A Krause","year":"2010","unstructured":"Krause A, Perona P, Gomes R (2010) Discriminative clustering by regularized information maximization. Adv Neural Inf Process Syst 23(9):775\u2013783","journal-title":"Adv Neural Inf Process Syst"},{"issue":"8","key":"3247_CR45","first-page":"129","volume":"20","author":"J Blitzer","year":"2007","unstructured":"Blitzer J, Crammer K, Kulesza A et al (2007) Learning bounds for domain adaptation. Adv Neural Inf Process Syst 20(8):129\u2013136","journal-title":"Adv Neural Inf Process Syst"},{"issue":"11","key":"3247_CR46","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1111\/psyp.12258","volume":"51","author":"E Labkovsky","year":"2014","unstructured":"Labkovsky E, Rosenfeld JP (2014) A novel dual probe complex trial protocol for detection of concealed information. Psychophysiology 51(11):1122\u20131130","journal-title":"Psychophysiology"},{"key":"3247_CR47","doi-asserted-by":"crossref","unstructured":"Zheng W, Zhang Y, Zhu J, et al (2015) Transfer components between subjects for EEG-based emotion recognition. In: International conference on affective computing and intelligent interaction, pp 917\u2013922","DOI":"10.1109\/ACII.2015.7344684"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03247-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03247-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03247-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T09:38:19Z","timestamp":1788169099000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03247-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,27]]},"references-count":47,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["3247"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03247-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,27]]},"assertion":[{"value":"21 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"411"}}