دانلود مقاله انگلیسی رایگان:رمزگذار خودکار گرافیکی برای استخراج نمودار مبتنی بر EEG - 2021
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  • Graph Variational Auto-Encoder for Deriving EEG-based Graph Embedding Graph Variational Auto-Encoder for Deriving EEG-based Graph Embedding
    Graph Variational Auto-Encoder for Deriving EEG-based Graph Embedding

    سال انتشار:

    2021


    عنوان انگلیسی مقاله:

    Graph Variational Auto-Encoder for Deriving EEG-based Graph Embedding


    ترجمه فارسی عنوان مقاله:

    رمزگذار خودکار گرافیکی برای استخراج نمودار مبتنی بر EEG


    منبع:

    Sciencedirect - Elsevier - Pattern Recognition Available online 22 July 2021, 108202


    نویسنده:

    Tina Behrouzi


    چکیده انگلیسی:

    Graph embedding is an effective method for deriving low-dimensional representations of graph data. The power of graph deep learning methods to characterize electroencephalogram (EEG) graph embedding is still in question. We designed a novel graph variational auto-encoder (GVAE) method to extract nodal features of brain functional connections. A new decoder model for the GVAEs network is proposed, which considers the node neighborhood of the reconstructed adjacency matrix. The GVAE is applied and tested on 3 biometric databases which contain 64 to 9 channels’ EEG recordings. For all datasets, promising results with more than 95% accuracy and consider- ably low computational cost are achieved compared to state-of-the-art user identification methods. The proposed GVAE is robust to a limited number of nodes and stable to users’ task performance. Moreover, we developed a Abstract Graph embedding is an effective method for deriving low-dimensional representations of graph data. The power of graph deep learning methods to characterize electroencephalogram (EEG) graph embedding is still in ques- tion. We designed a novel graph variational auto-encoder (GVAE) method to extract nodal features of brain functional connections. A new decoder model for the GVAEs network is proposed, which considers the node neighborhood of the reconstructed adjacency matrix. The GVAE is applied and tested on 3 biometric databases which contain 64 to 9 channels’ EEG recordings. For all datasets, promising results with more than 95% accuracy and consider- ably low computational cost are achieved compared to state-of-the-art user identification methods. The proposed GVAE is robust to a limited number of nodes and stable to users’ task performance. Moreover, we developed atraditional variational auto-encoder to demonstrate that more accurate features can be obtained when observing EEG-based brain connectivity from a graph perspective. Email addresses: tina.behrouzi@mail.utoronto.ca (Tina Behrouzi),dimitris@comm.utoronto.ca (Dimitrios Hatzinakos)Preprint submitted to Pattern Recognition July 20, 2021
    Keywords: Biometrics | Functional connectivity | Electroencephalogram (EEG) | Graph Variational Auto Encoder (GVAE) | Graph deep learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 39
    حجم فایل: 1802 کیلوبایت

    قیمت: رایگان


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