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

Overview of attention for article published in Sensors, November 2015
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  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

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1 patent
q&a
1 Q&A thread

Citations

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119 Dimensions

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160 Mendeley
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Title
Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task
Published in
Sensors, November 2015
DOI 10.3390/s151129015
Pubmed ID
Authors

Noor Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Mohd Shabiul Islam, Javier Escudero

Abstract

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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 160 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 <1%
United Kingdom 1 <1%
Unknown 158 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 24%
Student > Master 27 17%
Student > Bachelor 15 9%
Professor > Associate Professor 9 6%
Lecturer 8 5%
Other 24 15%
Unknown 38 24%
Readers by discipline Count As %
Engineering 69 43%
Computer Science 21 13%
Neuroscience 6 4%
Psychology 6 4%
Physics and Astronomy 3 2%
Other 12 8%
Unknown 43 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 September 2021.
All research outputs
#6,374,203
of 25,374,917 outputs
Outputs from Sensors
#2,908
of 24,303 outputs
Outputs of similar age
#88,905
of 392,665 outputs
Outputs of similar age from Sensors
#33
of 160 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 24,303 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 392,665 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 160 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.