Neural network analysis of electroencephalograms based on their graphical representation
Электронный архив ТПУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Neural network analysis of electroencephalograms based on their graphical representation
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| Автор |
Bragin, Aleksandr Dmitrievich
Spitsin, Vladislav Vladimirovich |
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| Тематика |
motor imagery recognition
electroencephalogram Gramian Angular Field Markov Transition Field Convolutional Neural Network распознавание изображений изображения электроэнцефалограммы марковские поля сверточные нейронные сети преобразование сигналов |
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| Описание |
The article is devoted to the problem of recognition of motor imagery based on electroencephalogram (EEG) signals, which is associated with many difficulties, such as the physical and mental state of a person, measurement accuracy, etc. Artificial neural networks are a good tool in solving this class of problems. Electroencephalograms are time signals, Gramian Angular Fields (GAF) and Markov Transition Field (MTF) transformations are used to represent time series as images. The paper shows the possibility of using GAF and MTF EEG signal transforms for recognizing motor patterns, which is further applicable, for example, in building a brain-computer interface.
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| Дата |
2020-01-23T09:13:05Z
2020-01-23T09:13:05Z 2019 |
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| Тип |
Conference Paper
Published version (info:eu-repo/semantics/publishedVersion) Conference paper (info:eu-repo/semantics/conferencePaper) |
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| Идентификатор |
Bragin A. D. Neural network analysis of electroencephalograms based on their graphical representation / A. D. Bragin, V. V. Spitsin // 14th International Forum on Strategic Technology (IFOST-2019), October 14-17, 2019, Tomsk, Russia : [proceedings]. — Tomsk : TPU Publishing House, 2019. — [С. 302-305].
http://earchive.tpu.ru/handle/11683/57471 |
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| Язык |
en
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| Связанные ресурсы |
14th International Forum on Strategic Technology (IFOST-2019), October 14-17, 2019, Tomsk, Russia : [proceedings]. — Tomsk, 2019.
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| Права |
Open access (info:eu-repo/semantics/openAccess)
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