© 2016

EEG Signal Analysis and Classification

Techniques and Applications


  • Presents innovative methodologies in two emerging fields, including epileptic seizure detection and mental state identification for brain computer interface

  • Discusses the applications of developed methods in real-time benchmark databases and provides experimental evaluation results to assess the efficacy of such methods

  • Shows researchers and practitioners how to improve the existing systems to increase benefits for medical analysis and management


Part of the Health Information Science book series (HIS)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Siuly Siuly, Yan Li, Yanchun Zhang
      Pages 3-21
    3. Siuly Siuly, Yan Li, Yanchun Zhang
      Pages 23-41
    4. Siuly Siuly, Yan Li, Yanchun Zhang
      Pages 43-61
  3. Techniques for the Diagnosis of Epileptic Seizures from EEG Signals

    1. Front Matter
      Pages 63-63
    2. Siuly Siuly, Yan Li, Yanchun Zhang
      Pages 65-82
    3. Siuly Siuly, Yan Li, Yanchun Zhang
      Pages 83-97
  4. Methods for Identifying Mental States in Brain Computer Interface Systems

  5. Discussions, Future Directions and Conclusions

    1. Front Matter
      Pages 245-245
    2. Siuly Siuly, Yan Li, Yanchun Zhang
      Pages 247-256

About this book


This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use. 

Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. 
Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several real-time benchmark databases.

This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals.


Electroencephalogram (EEG) Epileptic seizure Feature extraction Classification Brain computer interface (BCI) Motor imagery (MI) Clustering technique (CT) Simple random sampling (SRS) Cross-correlation (CC) technique Optimum allocation technique Least square supper vector machine (LS-SVM) Logistic regression (LR) Kernal logistic regression (KLR) Optimum allocation sampling k-NN Multinomial logistic regression with a ridge estimator Support vector machine (SVM) Naive Bayes method

Authors and affiliations

  1. 1.Centre for Applied InformaticsCollege of Engineering and Science, Victoria UniversityMelbourneAustralia
  2. 2.School of Agricultural, Computational and Environmental Sciences, Engineering and SciencesUniversity of Southern QueenslandToowoombaAustralia
  3. 3.Centre for Applied Informatics, College of Engineering and ScienceVictoria UniversityMelbourneAustralia

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