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Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

  • Hafeez Ullah Amin
  • Aamir Saeed MalikEmail author
  • Rana Fayyaz Ahmad
  • Nasreen Badruddin
  • Nidal Kamel
  • Muhammad Hussain
  • Weng-Tink Chooi
Scientific Paper

Abstract

This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task—Raven’s advance progressive metric test and (2) the EEG signals recorded in rest condition—eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53–3.06 and 3.06–6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.

Keywords

Discrete wavelet transform (DWT) Machine learning classifiers Electroencephalography (EEG) Cognitive task 

Notes

Acknowledgments

This research work has been supported by University Research Internal Funding (URIF: 0153AA-B26), Universiti Teknologi PETRONAS; the Fundamental Research Grant Scheme (Ref: FRGS/1/2014/TK03/UTP/02/1), Ministry of Education (MOE), Malaysia and by NSTIP strategic technologies programs, Grant number (12-INF2582-02), in the Kingdom of Saudi Arabia.

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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2015

Authors and Affiliations

  • Hafeez Ullah Amin
    • 1
  • Aamir Saeed Malik
    • 1
    Email author
  • Rana Fayyaz Ahmad
    • 1
  • Nasreen Badruddin
    • 1
  • Nidal Kamel
    • 1
  • Muhammad Hussain
    • 2
  • Weng-Tink Chooi
    • 3
  1. 1.Department of Electrical & Electronic Engineering, Centre for Intelligent Signal & Imaging Research (CISIR)Universiti Teknologi PETRONASTronohMalaysia
  2. 2.Department of Computer Science, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  3. 3.Advanced Medical and Dental Institute (AMDI)Universiti Sains MalaysiaKepala BatasMalaysia

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