An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals

  • Siuly SiulyEmail author
  • Varun Bajaj
  • Abdulkadir Sengur
  • Yanchun Zhang
Research Article


This paper addresses an advanced analysis system for the identification of alcoholic brain states from electroencephalogram (EEG) data in an automatic way. This study introduces an optimum allocation based sampling (OAS) scheme to discover the most favourable representative data points from every single time-window of each EEG signal considering the minimal variability of the observations. Combining all representative samples of each time-window in a set, some statistical features are extracted from every set of each class. The Mann-Whitney U test is used to assess whether each of the features is significant between the two classes (e.g., alcoholic and control). In order to evaluate the effectiveness of the OAS-based features, four well-known machine learning methods (decision table, support vector machine (SVM), k-nearest neighbor (k-NN) and logistic regression) are considered for identification of alcoholic brain state. The experimental results on the UCI KDD (i.e., UCI knowledge discovery in databases) database demonstrate that the OAS based decision table algorithm yields the highest accuracy of 99.58% with a low false alarm rate 0.40%, which is an improvement of up to 9.58% over the existing algorithms. A proposed analysis system can be used to detect alcoholism and also to determine the level of alcoholism-related changes in EEG signals.


Electroencephalogram (EEG) alcoholism optimum allocation technique feature extraction decision table 


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This work was supported by National Natural Science Foundation of China (No. 61332013) and the Australian Research Council (ARC) Linkage Project (No. LP100200682) and Discovery Project (No. DP140100841)


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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Gmbh Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Sustainable Industries & Liveable CitiesVictoria UniversityMelbourneAustralia
  2. 2.Discipline of Electronics and Communication EngineeringPDPM Indian Institute of Information Technology, Design and ManufacturingJabalpurIndia
  3. 3.Deptartement of Electrical and Electronics Engineering, Faculty of TechnologyFirat UniversityElazigTurkey
  4. 4.Cyberspace Institute of Advanced Technology (CIAT)Guangzhou UniversityGuangzhouChina

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