{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T14:17:29Z","timestamp":1788963449721,"version":"build-2803163510"},"reference-count":42,"publisher":"IOP Publishing","issue":"5","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61763022"],"award-info":[{"award-number":["61763022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Handwriting imagery (HI) based on electroencephalography (EEG) offers a non-invasive route to text input and complex intention expression for brain-computer interfaces (BCIs). However, HI EEG decoding is challenged by low signal-to-noise ratios, non-stationarity, cross-session distribution shifts, and the coexistence of continuous temporal trends and local high-response patterns.\n                    <jats:italic>Approach<\/jats:italic>\n                    . We propose a Dual-View Representation Decoupling Network (DRDNet) for HI EEG classification under within-subject cross-session and cross-subject evaluation settings. DRDNet first constructs two complementary temporal views from spatial EEG features using average and max pooling, corresponding to smooth trend-oriented and salient response-oriented representations. These views are then modeled by a bidirectional Mamba encoder and a Transformer encoder, respectively, and adaptively integrated through a time-step-level dynamic fusion mechanism followed by long short-term memory-based temporal aggregation. The method is evaluated on two tasks from a public HI EEG dataset: Chinese character stroke HI (CCSHI) and pinyin single-vowel HI (SVHI).\n                    <jats:italic>Main results<\/jats:italic>\n                    . DRDNet achieved average accuracies of 67.74% and 62.51%, with Cohen\u2019s kappa scores of 0.5968 and 0.5502, on CCSHI and SVHI, respectively. Under the cross-subject setting, DRDNet also achieved the best average accuracies of 62.54% and 54.40% on CCSHI and SVHI, respectively. DRDNet outperformed seven representative EEG decoding baselines under both evaluation settings. Confusion matrices, feature visualization, ablation studies, structural variant analysis, and complexity evaluation further showed improved feature separability and a favorable balance between decoding performance and computational efficiency.\n                    <jats:italic>Significance<\/jats:italic>\n                    . The results indicate that decoupling HI EEG into complementary temporal views and matching them with heterogeneous temporal encoders provides an effective representation learning strategy for robust non-invasive handwriting BCI decoding.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ae9bef","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T22:51:36Z","timestamp":1787179896000},"page":"056016","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DRDNet: a dual-view representation decoupling network for handwriting imagery EEG classification"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1027-378X","authenticated-orcid":true,"given":"Zhihao","family":"Li","sequence":"first","affiliation":[{"name":"Kunming University of Science and Technology"},{"name":"Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"Kunming University of Science and Technology"},{"name":"Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haichen","family":"Lu","sequence":"additional","affiliation":[{"name":"Kunming University of Science and Technology"},{"name":"Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9878-0513","authenticated-orcid":true,"given":"Rongzhang","family":"Luo","sequence":"additional","affiliation":[{"name":"Kunming University of Science and Technology"},{"name":"Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianwen","family":"Li","sequence":"additional","affiliation":[{"name":"Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology"},{"name":"Kunming University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology"},{"name":"Kunming University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4820-6337","authenticated-orcid":true,"given":"Yunfa","family":"Fu","sequence":"additional","affiliation":[{"name":"Kunming University of Science and Technology"},{"name":"Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"jneae9befbib1","author":"Wolpaw","year":"2012","type":"book"},{"key":"jneae9befbib2","doi-asserted-by":"publisher","first-page":"6510","DOI":"10.1038\/s41467-022-33611-3","type":"journal-article","article-title":"Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis","volume":"13","author":"Metzger","year":"2022","journal-title":"Nat. Commun."},{"key":"jneae9befbib3","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1038\/nature11076","type":"journal-article","article-title":"Reach and grasp by people with tetraplegia using a neurally controlled robotic arm","volume":"485","author":"Hochberg","year":"2012","journal-title":"Nature"},{"key":"jneae9befbib4","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1038\/s41586-023-06377-x","type":"journal-article","article-title":"A high-performance speech neuroprosthesis","volume":"620","author":"Willett","year":"2023","journal-title":"Nature"},{"key":"jneae9befbib5","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1056\/NEJMoa2027540","type":"journal-article","article-title":"Neuroprosthesis for decoding speech in a paralyzed person with anarthria","volume":"385","author":"Moses","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"jneae9befbib6","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1038\/s42256-023-00714-5","type":"journal-article","article-title":"Decoding speech perception from non-invasive brain recordings","volume":"5","author":"D\u00e9fossez","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"jneae9befbib7","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aaf12e","type":"journal-article","article-title":"A comprehensive review of EEG-based brain\u2013computer interface paradigms","volume":"16","author":"Abiri","year":"2019","journal-title":"J. Neural Eng."},{"key":"jneae9befbib8","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/5.939829","type":"journal-article","article-title":"Motor imagery and direct brain-computer communication","volume":"89","author":"Pfurtscheller","year":"2001","journal-title":"Proc. IEEE"},{"key":"jneae9befbib9","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jneumeth.2015.01.033","type":"journal-article","article-title":"Performance variation in motor imagery brain\u2013computer interface: a brief review","volume":"243","author":"Ahn","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"jneae9befbib10","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2019.100003","type":"journal-article","article-title":"Signal processing techniques for motor imagery brain computer interface: a review","volume":"1\u20132","author":"Aggarwal","year":"2019","journal-title":"Array"},{"key":"jneae9befbib11","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.3390\/s19061423","type":"journal-article","article-title":"EEG-based brain-computer interfaces using motor-imagery: techniques and challenges","volume":"19","author":"Padfield","year":"2019","journal-title":"Sensors"},{"key":"jneae9befbib12","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1038\/s41586-021-03506-2","type":"journal-article","article-title":"High-performance brain-to-text communication via handwriting","volume":"593","author":"Willett","year":"2021","journal-title":"Nature"},{"key":"jneae9befbib13","doi-asserted-by":"publisher","first-page":"4230","DOI":"10.1109\/TNSRE.2024.3492191","type":"journal-article","article-title":"Reconstructing multi-stroke characters from brain signals toward generalizable handwriting brain\u2013computer interfaces","volume":"32","author":"Yang","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneae9befbib14","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1038\/s41562-025-02157-x","type":"journal-article","article-title":"Human motor cortex encodes complex handwriting through a sequence of stable neural states","volume":"9","author":"Qi","year":"2025","journal-title":"Nat. Hum. Behav."},{"key":"jneae9befbib15","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1038\/s41597-026-06708-3","type":"journal-article","article-title":"An EEG dataset for handwriting imagery decoding of chinese character strokes and pinyin single vowels","volume":"13","author":"Wang","year":"2026","journal-title":"Sci. Data"},{"key":"jneae9befbib16","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac01a0","type":"journal-article","article-title":"Online recognition of handwritten characters from scalp-recorded brain activities during handwriting","volume":"18","author":"Pei","year":"2021","journal-title":"J. Neural Eng."},{"key":"jneae9befbib17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109132","type":"journal-article","article-title":"Handwritten character classification from EEG through continuous kinematic decoding","volume":"182","author":"Crell","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"jneae9befbib18","article-title":"Handwriting decoding as a challenging motor imagery task for EEG foundation models","author":"Ravishankar","year":"2025","type":"other"},{"key":"jneae9befbib19","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1109\/TBME.2019.2913914","type":"journal-article","article-title":"Transfer learning for brain\u2013computer interfaces: a euclidean space data alignment approach","volume":"67","author":"He","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"jneae9befbib20","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2025.103069","type":"journal-article","article-title":"A reliability-enhanced brain\u2013computer interface via mixture-of-graphs-driven information fusion","volume":"120","author":"Dai","year":"2025","journal-title":"Inf. Fusion"},{"key":"jneae9befbib21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-01368-2","type":"journal-article","article-title":"Cortical sensorimotor activity in the execution and suppression of discrete and rhythmic movements","volume":"11","author":"Hervault","year":"2021","journal-title":"Sci. Rep."},{"key":"jneae9befbib22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121612","type":"journal-article","article-title":"A cross-session motor imagery classification method based on riemannian geometry and deep domain adaptation","volume":"237","author":"Liu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"jneae9befbib23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102172","type":"journal-article","article-title":"Deep learning for motor imagery EEG-based classification: a review","volume":"63","author":"Al-saegh","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"jneae9befbib24","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","type":"journal-article","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"jneae9befbib25","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","type":"journal-article","article-title":"EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"jneae9befbib26","doi-asserted-by":"publisher","first-page":"2773","DOI":"10.1109\/TNSRE.2020.3048106","type":"journal-article","article-title":"EEG-inception: a novel deep convolutional neural network for assistive ERP-based brain-computer interfaces","volume":"28","author":"Santamar\u00eda-v\u00e1zquez","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneae9befbib27","type":"book","article-title":"Attention is all you need","volume":"Vol. 30","author":"Vaswani","year":"2017"},{"key":"jneae9befbib28","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","type":"journal-article","article-title":"EEG conformer: convolutional transformer for EEG decoding and visualization","volume":"31","author":"Song","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneae9befbib29","doi-asserted-by":"publisher","first-page":"2249","DOI":"10.1109\/TII.2022.3197419","type":"journal-article","article-title":"Physics-informed attention temporal convolutional network for EEG-based motor imagery classification","volume":"19","author":"Altaheri","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"jneae9befbib30","doi-asserted-by":"publisher","first-page":"12113","DOI":"10.1109\/TPAMI.2023.3275156","type":"journal-article","article-title":"Multimodal learning with transformers: a survey","volume":"45","author":"Xu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"jneae9befbib31","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108504","type":"journal-article","article-title":"Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding","volume":"175","author":"Ma","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"jneae9befbib32","article-title":"Mamba: linear-time sequence modeling with selective state spaces","author":"Gu","year":"2024","type":"other"},{"key":"jneae9befbib33","first-page":"3655","type":"conference-proceedings","article-title":"SST: multi-scale hybrid mamba-transformer experts for time series forecasting","author":"Xu","year":"2025"},{"key":"jneae9befbib34","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1111\/nyas.15288","type":"journal-article","article-title":"MI\u2010mamba: a hybrid motor imagery electroencephalograph classification model with mamba\u2019s global scanning","volume":"1544","author":"Guo","year":"2025","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"jneae9befbib35","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/s11571-025-10287-1","type":"journal-article","article-title":"Mifnet: a MamBa-based interactive frequency convolutional neural network for motor imagery decoding","volume":"19","author":"Yang","year":"2025","journal-title":"Cogn. Neurodyn"},{"key":"jneae9befbib36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107816","type":"journal-article","article-title":"EEGMamba: an EEG foundation model with mamba","volume":"192","author":"Wang","year":"2025","journal-title":"Neural Netw."},{"key":"jneae9befbib37","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127063","type":"journal-article","article-title":"RoFormer: enhanced transformer with rotary position embedding","volume":"568","author":"Su","year":"2024","journal-title":"Neurocomputing"},{"key":"jneae9befbib38","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","type":"journal-article","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"jneae9befbib39","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","type":"journal-article","article-title":"EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"jneae9befbib40","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-71118-7","type":"journal-article","article-title":"CTNet: a convolutional transformer network for EEG-based motor imagery classification","volume":"14","author":"Zhao","year":"2024","journal-title":"Sci. Rep."},{"key":"jneae9befbib41","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/adc5a3","type":"journal-article","article-title":"LGFormer: integrating local and global representations for EEG decoding","volume":"22","author":"Yang","year":"2025","journal-title":"J. Neural Eng."},{"key":"jneae9befbib42","first-page":"2579","type":"journal-article","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Journal of Neural Engineering"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ae9bef","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ae9bef\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ae9bef","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ae9bef\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ae9bef\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ae9bef\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ae9bef\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ae9bef\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T13:58:55Z","timestamp":1788962335000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ae9bef"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,9,9]]},"published-print":{"date-parts":[[2026,10,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1741-2552\/ae9bef","relation":{},"ISSN":["1741-2560","1741-2552"],"issn-type":[{"value":"1741-2560","type":"print"},{"value":"1741-2552","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]},"assertion":[{"value":"DRDNet: a dual-view representation decoupling network for handwriting imagery EEG classification","name":"article_title","label":"Article Title"},{"value":"Journal of Neural Engineering","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-05-10","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-19","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-09","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}