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Classification of EEG Signals Based on Image Representation of Statistical Features

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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Fig. 1.
Fig. 2.

Notes

  1. 1.

    * J. Ashford and J. J. Bird—co-first authors.

  2. 2.

    In this work we standardised the number of samples within each window to \(N = 150\). This means that quarter-windows have either \(n = 37\) or \(n = 38\) observations.

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Ashford, J., Bird, J.J., Campelo, F., Faria, D.R. (2020). Classification of EEG Signals Based on Image Representation of Statistical Features. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_37

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