Electroencephalogram Analysis Based on Gramian Angular FieldTransformation

Electroencephalogram Analysis Based on Gramian Angular FieldTransformation

Bibliografiska uppgifter
Parent link:CEUR Workshop Proceedings: Online Proceedings for Scientific Conferences and Workshops
Vol. 2485 : GraphiCon 2019. Computer Graphics and Vision.— 2019.— [P. 273-275]
Huvudupphovsman: Bragin A. D. Aleksandr Dmitrievich
Institutionell upphovsman: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Övriga upphovsmän: Spitsyn V. G. Vladimir Grigorievich
Sammanfattning: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
Språk:engelska
Publicerad: 2019
Ämnen:
электронный ресурс
труды учёных ТПУ
электроэнцефалограммы
Länkar:http://earchive.tpu.ru/handle/11683/57268
https://doi.org/10.30987/graphicon-2019-2-273-275
Materialtyp: Elektronisk Bokavsnitt
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=661413

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