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  • Authors: Giannopoulos, Anastasios;
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    Целью данной работы является разработка модели классификации моторных образов на основе инструментов глубокого обучения и преобразования Gramian Angular Field. В выпускной квалификационной работе рассмотрены современные подходы в проектировании систем BCI, извлечении признаков и классификации электроэнцефалограмм. Подготовлены обучающие данные и проведены эксперименты на различных архитектурах глубоких сетей для поиска оптимальных параметров входных данных, а также протестирован метод классификации, учитывающий соседние временные окна сигналов. Была разработана и оптимизирована модель нейронной сети, а также приведены результаты её обучения. The aim of this work is to develop a model of motor image classification based on deep learning tools and Gramian Angular Field transformation. In this paper the modern approaches in BCI system design, feature extraction and classification of electroencephalograms are considered. Training data were prepared and experiments were conducted on different deep network architectures to find the optimal input data parameters, and a classification method was tested that takes into account the neighboring time windows of signals. A neural network model was developed and optimized, and the results of its training were presented.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Electronic archive o...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Electronic archive o...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • Authors: Giannopoulos, Anastasios;
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Целью данной работы является разработка модели классификации моторных образов на основе инструментов глубокого обучения и преобразования Gramian Angular Field. В выпускной квалификационной работе рассмотрены современные подходы в проектировании систем BCI, извлечении признаков и классификации электроэнцефалограмм. Подготовлены обучающие данные и проведены эксперименты на различных архитектурах глубоких сетей для поиска оптимальных параметров входных данных, а также протестирован метод классификации, учитывающий соседние временные окна сигналов. Была разработана и оптимизирована модель нейронной сети, а также приведены результаты её обучения. The aim of this work is to develop a model of motor image classification based on deep learning tools and Gramian Angular Field transformation. In this paper the modern approaches in BCI system design, feature extraction and classification of electroencephalograms are considered. Training data were prepared and experiments were conducted on different deep network architectures to find the optimal input data parameters, and a classification method was tested that takes into account the neighboring time windows of signals. A neural network model was developed and optimized, and the results of its training were presented.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Electronic archive o...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Electronic archive o...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/