Electroencephalogram Analysis Based on Gramian Angular FieldTransformation
| Parent link: | CEUR Workshop Proceedings: Online Proceedings for Scientific Conferences and Workshops Vol. 2485 : GraphiCon 2019. Computer Graphics and Vision.— 2019.— [P. 273-275] |
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| Shrnutí: | Title screen This paper addresses the problem of motion imagery classification from electroencephalogram signals which related with manydifficulties such on human state, measurement accuracy, etc. Artificial neural networks are a good tool to solve such kind of problems.Electroencephalogram is time series signals therefore, a Gramian Angular Fields conversion has been applied to convert it into images.GAF conversion was used for classification EEG with Convolutional Neural Network (CNN). GAF images are represented as a Gramianmatrix where each element is the trigonometric sum between different time intervals. Grayscale images were applied for recognition toreduce numbers of neural network parameters and increase calculation speed. Images from each measuring channel were connectedinto one multi-channel image. This article reveals the possible usage GAF conversion of EEG signals to motion imagery recognition,which is beneficial in the applied fields, such as implement it in brain-computer interface |
| Jazyk: | angličtina |
| Vydáno: |
2019
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| Témata: | |
| On-line přístup: | http://earchive.tpu.ru/handle/11683/57268 https://doi.org/10.30987/graphicon-2019-2-273-275 |
| Médium: | Elektronický zdroj Kapitola |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=661413 |
MARC
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| 200 | 1 | |a Electroencephalogram Analysis Based on Gramian Angular FieldTransformation |f A. D. Bragin, V. G. Spitsyn | |
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| 300 | |a Title screen | ||
| 320 | |a [References: 17 tit.] | ||
| 330 | |a This paper addresses the problem of motion imagery classification from electroencephalogram signals which related with manydifficulties such on human state, measurement accuracy, etc. Artificial neural networks are a good tool to solve such kind of problems.Electroencephalogram is time series signals therefore, a Gramian Angular Fields conversion has been applied to convert it into images.GAF conversion was used for classification EEG with Convolutional Neural Network (CNN). GAF images are represented as a Gramianmatrix where each element is the trigonometric sum between different time intervals. Grayscale images were applied for recognition toreduce numbers of neural network parameters and increase calculation speed. Images from each measuring channel were connectedinto one multi-channel image. This article reveals the possible usage GAF conversion of EEG signals to motion imagery recognition,which is beneficial in the applied fields, such as implement it in brain-computer interface | ||
| 461 | |t CEUR Workshop Proceedings |o Online Proceedings for Scientific Conferences and Workshops | ||
| 463 | |t Vol. 2485 : GraphiCon 2019. Computer Graphics and Vision |o Proceedings of the 29th International Conference, Bryansk, Russia, September 23-26, 2019 |v [P. 273-275] |d 2019 | ||
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| 700 | 1 | |a Bragin |b A. D. |c specialist in the field of electrical engineering |c Postgraduate of Tomsk Polytechnic University |f 1991- |g Aleksandr Dmitrievich |3 (RuTPU)RU\TPU\pers\45579 | |
| 701 | 1 | |a Spitsyn |b V. G. |c specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University, Doctor of technical sciences |f 1948- |g Vladimir Grigorievich |3 (RuTPU)RU\TPU\pers\33492 |9 17160 | |
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