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

Hafeez Ullah Amin, Aamir Saeed Malik*, Rana Fayyaz Ahmad, Nasreen Badruddin, Nidal Kamel, Muhammad Hussain, Weng Tink Chooi

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

224 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)139-149
Number of pages11
JournalAustralasian Physical and Engineering Sciences in Medicine
Issue number1
Publication statusPublished - 30 Mar 2015


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


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