TY - GEN
T1 - Classification of EEG Signals Based on Image Representation of Statistical Features
AU - Ashford, Jodie
AU - Bird, Jordan
AU - Campelo, Felipe
AU - Faria, Diego
PY - 2019/8/30
Y1 - 2019/8/30
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.
AB - 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.
KW - Convolutional neural networks
KW - Electroencephalography
KW - Image recognition
KW - Machine learning
KW - Mental state classification
UR - https://link.springer.com/chapter/10.1007%2F978-3-030-29933-0_37
UR - http://www.scopus.com/inward/record.url?scp=85072870735&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-29933-0_37
DO - 10.1007/978-3-030-29933-0_37
M3 - Conference publication
SN - 978-3-030-29932-3
VL - 1043
T3 - Advances in Intelligent Systems and Computing
SP - 449
EP - 460
BT - Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019
A2 - Ju, Zhaojie
A2 - Zhou, Dalin
A2 - Gegov, Alexander
A2 - Yang, Longzhi
A2 - Yang, Chenguang
PB - Springer
T2 - 19th UK Workshop on Computational Intelligence
Y2 - 4 September 2019 through 6 September 2019
ER -