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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/65991

    Título
    EEG-inception: a novel deep convolutional Neural Network for assistive ERP-based brain-computer interfaces
    Autor
    SantaMaría Vazquez, EduardoAutoridad UVA
    Martínez Cagigal, VíctorAutoridad UVA Orcid
    Vaquerizo Villar, FernandoAutoridad UVA Orcid
    Hornero Sánchez, RobertoAutoridad UVA Orcid
    Año del Documento
    2020
    Editorial
    IEEE
    Descripción
    Producción Científica
    Documento Fuente
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, Diciembre, 2020, vol. 28 (12), pp. 2773 - 2782.
    Abstract
    In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data. In particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, including event-related potentials (ERP). In this study, we propose a novel CNN, called EEG-Inception, that improves the accuracy and calibration time of assistive ERP-based BCIs. To the best of our knowledge, EEG-Inception is the first model to integrate Inception modules for ERP detection, which combined efficiently with other structures in a light architecture, improved the performance of our approach. The model was validated in a population of 73 subjects, of which 31 present motor disabilities. Results show that EEG-Inception outperforms 5 previous approaches, yielding significant improvements for command decoding accuracy up to 16.0%, 10.7%, 7.2%, 5.7% and 5.1% in comparison to rLDA, xDAWN + Riemannian geometry, CNN-BLSTM, DeepConvNet and EEGNet, respectively. Moreover, EEG-Inception requires very few calibration trials to achieve state-of-the-art performances taking advantage of a novel training strategy that combines cross-subject transfer learning and fine-tuning to increase the feasibility of this approach for practical use in assistive applications.
    Palabras Clave
    Brain-computer interfaces
    Event-related potentials
    P300
    Deep learning
    Convolutional Neural Networks
    Inception
    Transfer learning
    Revisión por pares
    SI
    DOI
    10.1109/TNSRE.2020.3048106
    Patrocinador
    DPI2017-84280-R, 0378_AD_EEGWA_2_P
    Version del Editor
    https://ieeexplore.ieee.org/document/9311146
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/65991
    Tipo de versión
    info:eu-repo/semantics/acceptedVersion
    Derechos
    openAccess
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