Classification of EEG Signals Based on Image Representation of Statistical Features

Jodie Ashford, Jordan Bird, Felipe Campelo, Diego Faria

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 16×16 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
PublisherSpringer
Pages449-460
Number of pages12
Volume1043
ISBN (Electronic)978-3-030-29933-0
ISBN (Print)978-3-030-29932-3
DOIs
Publication statusE-pub ahead of print - 30 Aug 2019
Event19th UK Workshop on Computational Intelligence : UKCI 2019 - Portsmouth, United Kingdom
Duration: 4 Sep 20196 Sep 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1043
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference19th UK Workshop on Computational Intelligence
CountryUnited Kingdom
CityPortsmouth
Period4/09/196/09/19

Fingerprint

Electroencephalography
Neural networks
Image classification

Keywords

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

Cite this

Ashford, J., Bird, J., Campelo, F., & Faria, D. (2019). Classification of EEG Signals Based on Image Representation of Statistical Features. In Z. Ju, D. Zhou, A. Gegov, L. Yang, & C. Yang (Eds.), Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019 (Vol. 1043, pp. 449-460). (Advances in Intelligent Systems and Computing; Vol. 1043). Springer. https://doi.org/10.1007/978-3-030-29933-0_37
Ashford, Jodie ; Bird, Jordan ; Campelo, Felipe ; Faria, Diego. / Classification of EEG Signals Based on Image Representation of Statistical Features. Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. editor / Zhaojie Ju ; Dalin Zhou ; Alexander Gegov ; Longzhi Yang ; Chenguang Yang. Vol. 1043 Springer, 2019. pp. 449-460 (Advances in Intelligent Systems and Computing).
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title = "Classification of EEG Signals Based on Image Representation of Statistical Features",
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 16×16 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 = "Convolutional neural networks, Electroencephalography, Image recognition, Machine learning, Mental state classification",
author = "Jodie Ashford and Jordan Bird and Felipe Campelo and Diego Faria",
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Ashford, J, Bird, J, Campelo, F & Faria, D 2019, Classification of EEG Signals Based on Image Representation of Statistical Features. in Z Ju, D Zhou, A Gegov, L Yang & C Yang (eds), Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. vol. 1043, Advances in Intelligent Systems and Computing, vol. 1043, Springer, pp. 449-460, 19th UK Workshop on Computational Intelligence , Portsmouth, United Kingdom, 4/09/19. https://doi.org/10.1007/978-3-030-29933-0_37

Classification of EEG Signals Based on Image Representation of Statistical Features. / Ashford, Jodie; Bird, Jordan; Campelo, Felipe; Faria, Diego.

Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. ed. / Zhaojie Ju; Dalin Zhou; Alexander Gegov; Longzhi Yang; Chenguang Yang. Vol. 1043 Springer, 2019. p. 449-460 (Advances in Intelligent Systems and Computing; Vol. 1043).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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.

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Ashford J, Bird J, Campelo F, Faria D. Classification of EEG Signals Based on Image Representation of Statistical Features. In Ju Z, Zhou D, Gegov A, Yang L, Yang C, editors, Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. Vol. 1043. Springer. 2019. p. 449-460. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-29933-0_37