EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics
The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhancing the diagnosis of diseases. To address these issues, we propose a novel two-st...
Uložené v:
| Vydané v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5 |
|---|---|
| Hlavní autori: | , , , , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
06.04.2025
|
| Predmet: | |
| ISSN: | 2379-190X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhancing the diagnosis of diseases. To address these issues, we propose a novel two-stage approach named Self-Supervised State Reconstruction-Primed Riemannian Dynamics (EEG-ReMinD), which mitigates reliance on supervised learning and integrates inherent geometric features. This approach efficiently handles EEG data corruptions and reduces the dependency on labels. EEG-ReMinD utilizes self-supervised and geometric learning techniques, along with an attention mechanism, to analyze the temporal dynamics of EEG features within the framework of Riemannian geometry, referred to as Riemannian dynamics. Comparative analyses on both intact and corrupted datasets from two different neurodegenerative disorders underscore the enhanced performance of EEG-ReMinD. |
|---|---|
| AbstractList | The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhancing the diagnosis of diseases. To address these issues, we propose a novel two-stage approach named Self-Supervised State Reconstruction-Primed Riemannian Dynamics (EEG-ReMinD), which mitigates reliance on supervised learning and integrates inherent geometric features. This approach efficiently handles EEG data corruptions and reduces the dependency on labels. EEG-ReMinD utilizes self-supervised and geometric learning techniques, along with an attention mechanism, to analyze the temporal dynamics of EEG features within the framework of Riemannian geometry, referred to as Riemannian dynamics. Comparative analyses on both intact and corrupted datasets from two different neurodegenerative disorders underscore the enhanced performance of EEG-ReMinD. |
| Author | Wang, Zirui Song, Zhenxi Zhang, Zhiguo Liu, Yuxin Guo, Yi Xu, Guoyang Zhang, Min |
| Author_xml | – sequence: 1 givenname: Zirui surname: Wang fullname: Wang, Zirui organization: Harbin Institute of Technology,Shenzhen,China – sequence: 2 givenname: Zhenxi surname: Song fullname: Song, Zhenxi email: songzhenxi@hit.edu.cn organization: Harbin Institute of Technology,Shenzhen,China – sequence: 3 givenname: Yi surname: Guo fullname: Guo, Yi organization: Shenzhen People's Hospital,Department of Neurology,Shenzhen,China – sequence: 4 givenname: Yuxin surname: Liu fullname: Liu, Yuxin organization: Harbin Institute of Technology,Shenzhen,China – sequence: 5 givenname: Guoyang surname: Xu fullname: Xu, Guoyang organization: Harbin Institute of Technology,Shenzhen,China – sequence: 6 givenname: Min surname: Zhang fullname: Zhang, Min organization: Harbin Institute of Technology,Shenzhen,China – sequence: 7 givenname: Zhiguo surname: Zhang fullname: Zhang, Zhiguo organization: Harbin Institute of Technology,Shenzhen,China |
| BookMark | eNo1kNFKwzAUhqMouM29gRfxAVKTpm0S72SrU5g61l14N2JyukW2dKTpYODDW1HhwLn4Pn7O-YfowjceELplNGGMqrvnyUNVLTJVFDRJaZonjEopcqHO0FgJJXlOeSFFxs7RIOVCEabo-xUatu0npbQHcoC-ynJGlvDi_PQel36rvXF-g1-hC42FDXgIOroj4N7DUzCN_cFxG5pus8UV7GpSdQcIR9eCxVXUEfCy13wbQ2eiazxZBLfv2dLBXnvvtMfTk9d7Z9prdFnrXQvjvz1Cq8dyNXki87dZ_9ycOMUjkUWWWZErnaU6TzPKpCqYoKnhuRUpFUbxfsSHqrk1xsia81rWAkxhheUS-Ajd_MY6AFgf-nN0OK3_y-LfDapiwg |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1109/ICASSP49660.2025.10887579 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9798350368741 |
| EISSN | 2379-190X |
| EndPage | 5 |
| ExternalDocumentID | 10887579 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China funderid: 10.13039/501100001809 |
| GroupedDBID | 23M 6IE 6IF 6IH 6IK 6IL 6IM 6IN AAJGR AAWTH ABLEC ACGFS ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IJVOP IPLJI M43 OCL RIE RIL RIO RNS |
| ID | FETCH-LOGICAL-i93t-8644d759a42a524018961702c35d7207c93c937b9f3dccc8f33f8f7ec6d7d38e3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Nov 19 08:26:49 EST 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-8644d759a42a524018961702c35d7207c93c937b9f3dccc8f33f8f7ec6d7d38e3 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_10887579 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-April-6 |
| PublicationDateYYYYMMDD | 2025-04-06 |
| PublicationDate_xml | – month: 04 year: 2025 text: 2025-April-6 day: 06 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) |
| PublicationTitleAbbrev | ICASSP |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0008748 |
| Score | 2.2880259 |
| Snippet | The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Decoding Diseases EEG Electroencephalography Geometric Deep Learning Geometry Heuristic algorithms Neurodegenerative Disorders Riemannian Manifold Self-supervised Learning Signal processing Signal processing algorithms Speech processing Supervised learning Three-dimensional displays |
| Title | EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics |
| URI | https://ieeexplore.ieee.org/document/10887579 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ3LS8MwHMeDGyJ68TXxTQSvmTVpm8Sb7KEeHGXdYbfRJr_MgnbDbZ78402yzunBg9BDKE1LE_J7tPl9vghdB4YKGyVLu8RNRkKT5ySjQInOIYhzV10SKS82wXs9MRzKpCpW97UwAOA3n0HTNf2_fD1RC_epzK5w4fjrsoZqnMfLYq1vsyt4KLbQVQXRvHlq3adpEjr4pM0CadRcdf4lo-K9SHf3n8_fQ411PR5Ovj3NPtqA8gDt_EAJHqLPTueB9OG5KNt3uFO-OIxGOcaevaFh7OHSzrJhex1u25zT3QlXKj04hVdD0sXUGY4ZaOxDUOxS0zVgliROCUDjfgFvTugoK3F7KWc_a6BBtzNoPZJKWYEUks2JsEGQ5pHMQppF1qXfCum47FSxSHMacCWZPXguDdNKKWEYM8JwULHmmglgR6heTko4RljGOmQMTGhsakOjLDcm4NrGoBHjoaFwghpuHEfTJTtjtBrC0z_On6FtN1t-b0x8jur2HeECbaqPeTF7v_Qz_gUfx659 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ3LT8IwHMcbRePj4gvj25p4rc52o603w0OIQAhw4Ea29lck0UEEPPnH25YBevBgskOzrMvWpr_H1t_ni9BtYKiwUbK0S9zEJDRJQmIKlOgEgkLiqksi5cUmeLMpej3ZyorVfS0MAPjNZ3Dnmv5fvh6pmftUZle4cPx1uY42nHRWVq61NLyCh2IL3WQYzfta8anTaYUOP2nzQBrdLbr_ElLxfqSy988n2Ef5VUUebi19zQFag_QQ7f6ACR6hr3L5mbShMUxLj7icvjqQRjrAnr6hYeDx0s62YXsdLtms090JZzo9uANvhnRmY2c6JqCxD0KxS05XiFnScloAGreH8O6kjuIUl-aC9pM86lbK3WKVZNoKZCjZlAgbBmkeyTikcWSd-oOQjsxOFYs0pwFXktmDJ9IwrZQShjEjDAdV0FwzAewY5dJRCicIy4IOGQMTGpvc0ChOjAm4tlFoxHhoKJyivBvH_nhOz-gvhvDsj_PXaLvabdT79Vrz5RztuJnzO2UKFyhn3xcu0ab6nA4nH1d-9r8BYu-xxg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+of+the+...+IEEE+International+Conference+on+Acoustics%2C+Speech+and+Signal+Processing+%281998%29&rft.atitle=EEG-ReMinD%3A+Enhancing+Neurodegenerative+EEG+Decoding+through+Self-Supervised+State+Reconstruction-Primed+Riemannian+Dynamics&rft.au=Wang%2C+Zirui&rft.au=Song%2C+Zhenxi&rft.au=Guo%2C+Yi&rft.au=Liu%2C+Yuxin&rft.date=2025-04-06&rft.pub=IEEE&rft.eissn=2379-190X&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FICASSP49660.2025.10887579&rft.externalDocID=10887579 |