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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