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Frequency-response-based Wavelet
Decomposition for Extracting
Children’s Mismatch Negativity
Elicited by Uninterrupted Sound
Department of Mathematical Information Technology ,University of Jyväskylä,Jyväskylä 40014,Finland
Center for Intelligent Maintenance Systems,University of Cincinnati,OH 45221,USA
School of Psychology, Beijing Normal University,Beijing 100875,China
Department of Psychology,University of Jyväskylä, Jyväskylä 40014,Finland
Received 6 Apr 2011; Accepted 14 Sep 2011; doi: 10.5405/jmbe.908
Chairman:Hung-Chi Yang
Presenter: Yu-Kai Wang
Advisor: Dr. Yeou-Jiunn Chen
Date: 2013.3.6
Outline
Introduction
Purposes
Materials and Methods
Results
Conclusions
Introduction
Other types of activity that overlap MMN are not separated
in the time and/or frequency domain
To obtain pure MMN activity, researchers have used many
signal processing techniques
Digital filters
Wavelet decomposition (WLD)
Principal component analysis(PCA)
Independent component analysis(ICA)
Introduction
Wavelet Decomposition(WLD)
Which was especially designed for non-stationary signals
First factorizes the signal into several levels with a particular
wavelet
The coefficients of some of the levels are chosen to
reconstruct the desired signal
Can thus be regarded as a special band-pass filter
Purposes
Designs a paradigm based on the fact that
The magnitude of the frequency response of WLD and the
spectral properties of MMN conform to each other
To determine the type of wavelet
The number of levels the signal should be decomposed into
The levels required for the reconstruction
EEG recordings before WLD is performed
2-8.5 Hz was found to be the most
Optimal frequency band for MMN in their dataset