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

Amin, Hafeez Ullah and Malik, Aamir Saeed and Ahmad, Rana Fayyaz and Badruddin , Nasreen and Kamel, Nidal S. and Hussain, Muhammad and Chooi, Weng-Tink (2015) Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australasian Physical and Engineering Sciences in Medicine . ISSN 1879-5447

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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.

Item Type:Article
Uncontrolled Keywords:Discrete wavelet transform (DWT); Machine learning classifiers; Electroencephalography (EEG); Cognitive task
Subjects:Q Science > Q Science (General)
T Technology > T Technology (General)
Academic Subject One:Academic Department - Electrical And Electronics - Communications - Digital Communications - Digital Signal Processing
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
Research Institutes > Institute for Health Analytics
ID Code:11799
Deposited By: Dr Aamir Saeed Malik
Deposited On:07 Oct 2016 01:42
Last Modified:07 Oct 2016 01:42

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