Academic Journal
EEG Signal Classification Based on Multitask Diffusion Adaptation Over Customized Hyper-Networks.
| Τίτλος: | EEG Signal Classification Based on Multitask Diffusion Adaptation Over Customized Hyper-Networks. |
|---|---|
| Συγγραφείς: | Bahraini, Tahereh1 (AUTHOR) tahereh.bahraini67@sharif.edu, Shamsollahi, Mohammad B.1 (AUTHOR) mbshams@sharif.edu, Afkhaminia, Fatemeh1 (AUTHOR) fatemeh.afkhaminia@ee.sharif.edu |
| Πηγή: | International Journal of Pattern Recognition & Artificial Intelligence. Jul2026, p1. 48p. 16 Illustrations. |
| Θεματικοί όροι: | *Adaptive control systems, *Real-time computing, Signal classification, Adaptive signal processing, Neurologic examination, Robust statistics, Biomedical signal processing |
| Περίληψη: | The classification of multi-channel signals remains a challenging yet essential task in biomedical signal processing. Among these, electroencephalogram (EEG) signals stand out due to their potential to assist clinicians in diagnosing neurological conditions. Existing classification methods, however, often struggle with issues such as noise, variability across subjects, and the complexity of real-time data processing. This study introduces a novel classification framework grounded in the concept of a customized hyper-network. Within this architecture, hyper-nodes dynamically represent independent adaptive networks that collectively enhance the system’s flexibility and robustness. By integrating a classification strategy into the hyper-network structure, the proposed method demonstrates improved performance in handling real EEG signals, even when corrupted by noise. Extensive comparative experiments conducted on both synthetic and real-world EEG datasets validate the proposed approach. The results highlight its superior accuracy and adaptability in comparison to existing methods, underscoring the frameworks potential for real-time applications and clinical integration. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: EEG Signal Classification Based on Multitask Diffusion Adaptation Over Customized Hyper-Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bahraini%2C+Tahereh%22">Bahraini, Tahereh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tahereh.bahraini67@sharif.edu</i><br /><searchLink fieldCode="AR" term="%22Shamsollahi%2C+Mohammad+B%2E%22">Shamsollahi, Mohammad B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mbshams@sharif.edu</i><br /><searchLink fieldCode="AR" term="%22Afkhaminia%2C+Fatemeh%22">Afkhaminia, Fatemeh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fatemeh.afkhaminia@ee.sharif.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. Jul2026, p1. 48p. 16 Illustrations. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+classification%22">Signal classification</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+signal+processing%22">Adaptive signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Neurologic+examination%22">Neurologic examination</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+signal+processing%22">Biomedical signal processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The classification of multi-channel signals remains a challenging yet essential task in biomedical signal processing. Among these, electroencephalogram (EEG) signals stand out due to their potential to assist clinicians in diagnosing neurological conditions. Existing classification methods, however, often struggle with issues such as noise, variability across subjects, and the complexity of real-time data processing. This study introduces a novel classification framework grounded in the concept of a customized hyper-network. Within this architecture, hyper-nodes dynamically represent independent adaptive networks that collectively enhance the system’s flexibility and robustness. By integrating a classification strategy into the hyper-network structure, the proposed method demonstrates improved performance in handling real EEG signals, even when corrupted by noise. Extensive comparative experiments conducted on both synthetic and real-world EEG datasets validate the proposed approach. The results highlight its superior accuracy and adaptability in comparison to existing methods, underscoring the frameworks potential for real-time applications and clinical integration. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/s0218001425400099 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 48 StartPage: 1 Subjects: – SubjectFull: Adaptive control systems Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Signal classification Type: general – SubjectFull: Adaptive signal processing Type: general – SubjectFull: Neurologic examination Type: general – SubjectFull: Robust statistics Type: general – SubjectFull: Biomedical signal processing Type: general Titles: – TitleFull: EEG Signal Classification Based on Multitask Diffusion Adaptation Over Customized Hyper-Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bahraini, Tahereh – PersonEntity: Name: NameFull: Shamsollahi, Mohammad B. – PersonEntity: Name: NameFull: Afkhaminia, Fatemeh IsPartOfRelationships: – BibEntity: Dates: – D: 09 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02180014 Titles: – TitleFull: International Journal of Pattern Recognition & Artificial Intelligence Type: main |
| ResultId | 1 |