Selection of Mother Wavelet Function for Multi-Channel EEG Signals Analysis during a Working Memory Task

Noor Kamal Al-qazzaz, Sawal Hamid Bin Md. Ali, Siti Anom Ahmad, Md. Shabiul Islam, Javier Escudero

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 electroencephalography (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). Conducting ANOVA, we determined the MWT basis functions with 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.
Original languageEnglish
Pages (from-to)29015-29035
Number of pages21
JournalSensors
Volume15
Issue number11
DOIs
Publication statusPublished - Nov 2015

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