Electroencephalogram analysis based on gramian angular field transformation

A. D. Bragin, V. G. Spitsyn

Research output: Contribution to journalConference articlepeer-review


This paper addresses the problem of motion imagery classification from electroencephalogram signals which related with many difficulties 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 Gramian matrix where each element is the trigonometric sum between different time intervals. Grayscale images were applied for recognition to reduce numbers of neural network parameters and increase calculation speed. Images from each measuring channel were connected into 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.

Original languageEnglish
Pages (from-to)273-275
Number of pages3
JournalCEUR Workshop Proceedings
Publication statusPublished - 2019
Event29th International Conference on Computer Graphics and Vision, GraphiCon 2019 - Bryansk, Russian Federation
Duration: 23 Sep 201926 Sep 2019


  • Convolutional Neural Network
  • Electroencephalogram
  • Gramian Angular Field
  • Motor imagery recognition

ASJC Scopus subject areas

  • Computer Science(all)

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