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Classification of EEG Signals Based on Image Representation of Statistical Features

  • Jodie Ashford
  • Jordan J. BirdEmail author
  • Felipe Campelo
  • Diego R. Faria
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1043)

Abstract

This work presents an image classification approach to EEG brainwave classification. The proposed method is based on the representation of temporal and statistical features as a 2D image, which is then classified using a deep Convolutional Neural Network. A three-class mental state problem is investigated, in which subjects experience either relaxation, concentration, or neutral states. Using publicly available EEG data from a Muse Electroencephalography headband, a large number of features describing the wave are extracted, and subsequently reduced to 256 based on the Information Gain measure. These 256 features are then normalised and reshaped into a \(16\times 16\) grid, which can be expressed as a grayscale image. A deep Convolutional Neural Network is then trained on this data in order to classify the mental state of subjects. The proposed method obtained an out-of-sample classification accuracy of 89.38%, which is competitive with the 87.16% of the current best method from a previous work.

Keywords

Machine learning Convolutional neural networks Image recognition Mental state classification Electroencephalography 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jodie Ashford
    • 1
  • Jordan J. Bird
    • 1
    Email author
  • Felipe Campelo
    • 1
  • Diego R. Faria
    • 1
  1. 1.School of Engineering and Applied ScienceAston UniversityBirminghamUK

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