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Sensors 2015, 15(11), 29015-29035;

Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi Selangor 43600, Malaysia
Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, Baghdad University, Baghdad 47146, Iraq
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
Institute for Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan, Malaysia (UKM), 43600 Bangi, Selangor, Malaysia
Institute for Digital Communications; School of Engineering, University of Edinburgh, Edinburgh EH9 3JL, UK
Author to whom correspondence should be addressed.
Academic Editor: Patricia A. Broderick
Received: 26 August 2015 / Accepted: 4 October 2015 / Published: 17 November 2015
(This article belongs to the Section Biosensors)
Full-Text   |   PDF [1680 KB, uploaded 17 November 2015]   |  


We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions. View Full-Text
Keywords: electroencephalography; memory; wavelet; multi-resolution analysis; cross-correlation electroencephalography; memory; wavelet; multi-resolution analysis; cross-correlation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Al-Qazzaz, N.K.; Hamid Bin Mohd Ali, S.; Ahmad, S.A.; Islam, M.S.; Escudero, J. Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task. Sensors 2015, 15, 29015-29035.

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