Causality-Driven Convolutional Manifold Attention Network for Electroencephalogram Signal Decoding
Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study prop...
Uloženo v:
| Vydáno v: | IEEE transactions on pattern analysis and machine intelligence Ročník PP; s. 1 - 12 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
IEEE
27.10.2025
|
| Témata: | |
| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology. |
|---|---|
| AbstractList | Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology. Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology.Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology. |
| Author | Chen, Junxiang Wen, Guilin Hua, Changchun Wang, Fuwang Lu, Bin Fu, Rongrong |
| Author_xml | – sequence: 1 givenname: Bin orcidid: 0009-0000-3678-1214 surname: Lu fullname: Lu, Bin email: lubin@stumail.ysu.edu.cn organization: School of Electrical Engineering, Yanshan University, Qinhuangdao, China – sequence: 2 givenname: Junxiang orcidid: 0000-0002-8897-754X surname: Chen fullname: Chen, Junxiang email: juc91@pitt.edu organization: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA – sequence: 3 givenname: Fuwang surname: Wang fullname: Wang, Fuwang email: 20152622@neepu.edu.cn organization: School of Mechanical Engineering, Northeast Electric Power University, Jilin, China – sequence: 4 givenname: Guilin orcidid: 0000-0001-5940-2520 surname: Wen fullname: Wen, Guilin email: glwen@ysu.edu.cn organization: School of Mechanical Engineering, Yanshan University, Qinhuangdao, China – sequence: 5 givenname: Rongrong orcidid: 0000-0003-0350-7619 surname: Fu fullname: Fu, Rongrong email: frr1102@aliyun.com organization: School of Electrical Engineering, Yanshan University, Qinhuangdao, China – sequence: 6 givenname: Changchun orcidid: 0000-0001-6311-2112 surname: Hua fullname: Hua, Changchun email: cch@ysu.edu.cn organization: School of Electrical Engineering, Yanshan University, Qinhuangdao, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41144414$$D View this record in MEDLINE/PubMed |
| BookMark | eNpFkMlOwzAQQC0EgrL8AEIoRy4pHsdx7GNVVolNAs6Rk0yKwbWLnYD4expa4DTS6L2R5u2STecdEnIIdAxA1enTw-T2eswoy8eZYLnIYIOMGAiaKqbYJhlRECyVkskdshvjK6XAc5ptkx0OwDkHPiLVVPdRW9N9pWfBfKBLpt59eNt3xjttk1vtTOttk0y6Dt2wTO6w-_ThLWl9SM4t1l3w6GpcvGjrZ0HPk0czG9QzrH1j3GyfbLXaRjxYzz3yfHH-NL1Kb-4vr6eTm7QGxbq0kbpohZBFA1poJVVV1bRoCo0MWJO3Sg4LaGVLBS-gAVVnmayozilf_kuzPXKyursI_r3H2JVzE2u0Vjv0fSwzJnJFcwCxRI_XaF_NsSkXwcx1-Cp_uywBtgLq4GMM2P4hQMshfvkTvxzil-v4S-loJRlE_BeAQZFzkX0Dfq6Axg |
| CODEN | ITPIDJ |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7X8 |
| DOI | 10.1109/TPAMI.2025.3625631 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 2160-9292 1939-3539 |
| EndPage | 12 |
| ExternalDocumentID | 41144414 10_1109_TPAMI_2025_3625631 11217546 |
| Genre | orig-research Journal Article |
| GroupedDBID | --- -DZ -~X .DC 0R~ 29I 4.4 53G 5GY 6IK 97E AAJGR AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 UHB ~02 5VS 9M8 AAYXX ABFSI ADRHT AETEA AETIX AGSQL AI. AIBXA ALLEH CITATION FA8 H~9 IBMZZ ICLAB IFJZH RNI RZB VH1 NPM 7X8 |
| ID | FETCH-LOGICAL-c192t-d8a7f6687d1a6a989bbc07d7ae212d5f98bbc01f8f06471d19c338b0a50492903 |
| IEDL.DBID | RIE |
| ISSN | 0162-8828 1939-3539 |
| IngestDate | Wed Oct 29 11:34:17 EDT 2025 Wed Oct 29 02:22:14 EDT 2025 Sat Nov 29 06:59:19 EST 2025 Wed Nov 05 07:10:40 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c192t-d8a7f6687d1a6a989bbc07d7ae212d5f98bbc01f8f06471d19c338b0a50492903 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-8897-754X 0000-0003-0350-7619 0009-0000-3678-1214 0000-0001-5940-2520 0000-0001-6311-2112 |
| PMID | 41144414 |
| PQID | 3265905116 |
| PQPubID | 23479 |
| PageCount | 12 |
| ParticipantIDs | proquest_miscellaneous_3265905116 pubmed_primary_41144414 ieee_primary_11217546 crossref_primary_10_1109_TPAMI_2025_3625631 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-Oct-27 |
| PublicationDateYYYYMMDD | 2025-10-27 |
| PublicationDate_xml | – month: 10 year: 2025 text: 2025-Oct-27 day: 27 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | IEEE transactions on pattern analysis and machine intelligence |
| PublicationTitleAbbrev | TPAMI |
| PublicationTitleAlternate | IEEE Trans Pattern Anal Mach Intell |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0014503 |
| Score | 2.4926803 |
| Snippet | Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Brain modeling Causality Decoding Deep learning EEG Electroencephalography Feature extraction Invariant Representation Manifolds Motors Reliability Riemannian Manifold Semantics Symmetric matrices |
| Title | Causality-Driven Convolutional Manifold Attention Network for Electroencephalogram Signal Decoding |
| URI | https://ieeexplore.ieee.org/document/11217546 https://www.ncbi.nlm.nih.gov/pubmed/41144414 https://www.proquest.com/docview/3265905116 |
| Volume | PP |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2160-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014503 issn: 0162-8828 databaseCode: RIE dateStart: 19790101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDLZg4gAH3o_xUpC4oULfSY7TAMFh0yRA2q1K0xQmQTtt3X4_dtryOHDgVlVJWsVO_H1xbANcCqlkkAaxwzOunVB7gYNcJXRMqJDHuVK4QtliE3w4FOOxHDXB6jYWxhhjL5-Za3q0vvys1As6KrtBbIDWLoxXYZVzXgdrfbkMwsiWQUYIg0sceUQbIePKm-dRb_CIXNCPrnG_juKA6sOEyAQQC4S_DJKtsPI32LRG537rn7-7DZsNumS9Wh12YMUUu7DVVm5gzULehY0faQj3IO2rxdziced2Rrsf65fFstFJHG6giklevmesV1X17Ug2rG-PM4S87K6upEMjT9-UzYH9wZ4mr9T1FtktWcd9eLm_e-4_OE3tBUcj5qucTCiex7HgmadiJYVMU-2iPJVBW5dFuRT0wstFTtGqXuZJjWQ3dVWElMOXbnAAnaIszBEw10-xW-5xndGBUyh0lPppYChml3LJdOGqFUAyrVNsJJaauDKxkktIckkjuS7s00x_t2wmuQsXrdASXCHk9lCFKRfzBAFqRFnIPGxzWEvzq3erBMd_jHoC6_RxMlY-P4VONVuYM1jTy2oyn52jGo7FuVXDT8aW1zk |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT-MwEB4tXSSWA2-W7vIwEjeUkoeT2MeqgEDQCokicYscx4FKkKA25ffvjJMU9sCBWxTZluUZe77PngfAiZBKBmkQOXEWa4drL3CQq3DHcIU8zpXCFcoWm4hHI_H4KO-aYHUbC2OMsc5npkef9i0_K_WcrsrOEBugtePREvwMOfe9Olxr8WjAQ1sIGUEMbnJkEm2MjCvPxnf94TWyQT_s4YkdRgFViOHIBRAN8P9Mkq2x8jXctGbncv2bE96AtQZfsn6tEJvwwxRbsN7WbmDNVt6C1U-JCLchHaj5zCJy53xK5x8blMV7o5U43FAVk7x8yVi_qmr_SDaq_ccZgl52UdfSoZHfnpXNgv3K7idP1PUc-S3Zxx14uLwYD66cpvqCoxH1VU4mVJxHkYgzT0VKCpmm2kWJKoPWLgtzKeiHl4uc4lW9zJMa6W7qqhBJhy_dYBc6RVmYPWCun2K33It1RldOXOgw9dPAUNQuZZPpwmkrgOStTrKRWHLiysRKLiHJJY3kurBDK_3RslnkLhy3Qktwj9DDhypMOZ8lCFFDykPmYZvftTQXvVsl-PPFqEewcjUe3ia316Obv_CLJkKmy4_3oVNN5-YAlvV7NZlND60y_gMNvdmY |
| 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%3Ajournal&rft.genre=article&rft.atitle=Causality-Driven+Convolutional+Manifold+Attention+Network+for+Electroencephalogram+Signal+Decoding&rft.jtitle=IEEE+transactions+on+pattern+analysis+and+machine+intelligence&rft.au=Lu%2C+Bin&rft.au=Chen%2C+Junxiang&rft.au=Wang%2C+Fuwang&rft.au=Wen%2C+Guilin&rft.date=2025-10-27&rft.pub=IEEE&rft.issn=0162-8828&rft.spage=1&rft.epage=12&rft_id=info:doi/10.1109%2FTPAMI.2025.3625631&rft_id=info%3Apmid%2F41144414&rft.externalDocID=11217546 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0162-8828&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0162-8828&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0162-8828&client=summon |