Classification of EEG Signals Based on Image Representation of Statistical Features

Jodie Ashford, Jordan Bird, Felipe Campelo, Diego Faria

Research output: Chapter in Book/Published conference outputConference publication


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 16×16 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.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019
EditorsZhaojie Ju, Dalin Zhou, Alexander Gegov, Longzhi Yang, Chenguang Yang
Number of pages12
ISBN (Electronic)978-3-030-29933-0
ISBN (Print)978-3-030-29932-3
Publication statusE-pub ahead of print - 30 Aug 2019
Event19th UK Workshop on Computational Intelligence : UKCI 2019 - Portsmouth, United Kingdom
Duration: 4 Sept 20196 Sept 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference19th UK Workshop on Computational Intelligence
Country/TerritoryUnited Kingdom


  • Convolutional neural networks
  • Electroencephalography
  • Image recognition
  • Machine learning
  • Mental state classification


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