Výsledky vyhledávání - "Aigerim Keutayeva"

Akademický článek

Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs

Autor: Aigerim Keutayeva, Nail Fakhrutdinov, Berdakh Abibullaev

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)

Abstract Motor imagery electroencephalography (EEG) analysis is crucial for the development of effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the complexity of the data and inter-subject variability. This p

Externí odkaz: https://doaj.org/article/ad79cc91eb6c4b52bc1e32355d0252c5

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Akademický článek

Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey

Autor: Aigerim Keutayeva, Berdakh Abibullaev

Publikováno v: IEEE Access, Vol 12, Pp 62628-62647 (2024)

This work reviews the critical challenge of data scarcity in developing Transformer-based models for Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), specifically focusing on Motor Imagery (MI) decoding. While EEG-BCIs hold immens

Externí odkaz: https://doaj.org/article/dddfeed44f6d4fe29442a4d062745608

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Akademický článek

Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

Autor: Berdakh Abibullaev, Aigerim Keutayeva, Amin Zollanvari

Publikováno v: IEEE Access, Vol 11, Pp 127271-127301 (2023)

Brain-computer interfaces (BCIs) have undergone significant advancements in recent years. The integration of deep learning techniques, specifically transformers, has shown promising development in research and application domains. Transformers, which

Externí odkaz: https://doaj.org/article/69e97fd3a970414a82ff52becbb5c5bf

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Akademický článek

Exploring the Potential of Attention Mechanism-Based Deep Learning for Robust Subject-Independent Motor-Imagery Based BCIs

Autor: Aigerim Keutayeva, Berdakh Abibullaev

Publikováno v: IEEE Access, Vol 11, Pp 107562-107580 (2023)

This study explores the use of attention mechanism-based deep learning models to construct subject-independent motor-imagery based brain-computer interfaces (MI-BCIs), which present unique and intricate challenges from a machine learning perspective.

Externí odkaz: https://doaj.org/article/34bb4ce926024879ab394d2d16c03821

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Akademický článek

Integrating Machine Learning Model and Digital Twin System for Additive Manufacturing

Autor: Nursultan Jyeniskhan, Aigerim Keutayeva, Gani Kazbek, Md Hazrat Ali, Essam Shehab

Publikováno v: IEEE Access, Vol 11, Pp 71113-71126 (2023)

Additive manufacturing is a promising manufacturing process with diverse applications, but ensuring the quality and reliability of the manufactured products are key challenges. The digital twin has emerged as a technology solution to address these ch

Externí odkaz: https://doaj.org/article/d768160ce91844ad81799db875831ea7

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