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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
Material and Methods
 Figure 2 shows
 The frequency ranges of the levels are different from those
given in Table 1
 The magnitude responses are not as flat as those obtained
using an optimal band-pass digital filter
 The fifth and sixth levels are the optimal levels for
reconstructing MMN
Material and Methods
the optimal levels
Material and Methods
 The next step is to determine which wavelet is the most
appropriate for evaluating MMN
 The wavelet toolbox of MATLAB 7.1, used here for data
processing
 Daubechies wavelets
 Coiflets
 symlets
 Discrete Meyer wavelets
 Biorthogonal wavelets
 Reverse biorthogonal wavelets