Data selection in EEG signals classification

  • Shuaifang WangEmail author
  • Yan Li
  • Peng Wen
  • David Lai
Scientific Paper


The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69 % of the whole data and 29.5 % of the computation time but achieves a 94.5 % classification accuracy. The channel selection method based on the GE difference also gains a 91.67 % classification accuracy by using only 29.69 % of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design.


EEG Data selection Horizontal visibility graph (HVG) Principal component analysis (PCA) 


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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2015

Authors and Affiliations

  1. 1.Faculty of Health, Engineering and SciencesUniversity of Southern QueenslandToowoombaAustralia

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