Please use this identifier to cite or link to this item: http://earchive.tpu.ru/handle/11683/57268
Title: Electroencephalogram Analysis Based on Gramian Angular FieldTransformation
Authors: Bragin, Aleksandr Dmitrievich
Spitsyn, Vladimir Grigorievich
Keywords: электроэнцефалограммы
Issue Date: 2019
Publisher: Томский политехнический университет
Citation: Bragin A. D. Electroencephalogram Analysis Based on Gramian Angular FieldTransformation / A. D. Bragin, V. G. Spitsyn // CEUR Workshop Proceedings. — 2019. — Vol. 2485 : GraphiCon 2019. Computer Graphics and Vision. — [P. 273-275].
Abstract: 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
URI: http://earchive.tpu.ru/handle/11683/57268
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