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Frequency-response-based Wavelet Decomposition for Extracting Childrens Mismatch Negativity Elicited by Uninterrupted Sound Department of Mathematical Information Technology ,University of Jyvskyl,Jyvskyl 40014,Finland Center for Intelligent Maintenance Systems,University of Cincinnati,OH 45221,USASchool of Psychology, Beijing Normal University,Beijing 100875,ChinaDepartment of Psychology,University of Jyvskyl, Jyvskyl 40014,FinlandReceived 6 Apr 2011; Accepted 14 Sep 2011; doi: 10.5405/jmbe.908 Chairman:Hung-Chi YangPresenter: Yu-Kai WangAdvisor: Dr. Yeou-Jiunn ChenDate: 2013.3.6

Outline

IntroductionPurposesMaterials and MethodsResultsConclusions

Introduction

Event-related potentials (ERPs)Applied to study the automatic auditory brain functions related to discriminationPerception in the brain of children with delayed language developmentAn ERP component, called mismatch negativity (MMN)

IntroductionFigure 1 Shows an oddball paradigm

the repeated standard stimulithe deviant stimuliThe standard sweepthe deviant sweepIntroductionOther types of activity that overlap MMN are not separated in the time and/or frequency domainTo obtain pure MMN activity, researchers have used many signal processing techniquesDigital filtersWavelet decomposition (WLD)Principal component analysis(PCA)Independent component analysis(ICA)

IntroductionWavelet Decomposition(WLD)Which was especially designed for non-stationary signalsFirst factorizes the signal into several levels with a particular waveletThe coefficients of some of the levels are chosen to reconstruct the desired signalCan thus be regarded as a special band-pass filterPurposesDesigns a paradigm based on the fact thatThe magnitude of the frequency response of WLD and the spectral properties of MMN conform to each otherTo determine the type of waveletThe number of levels the signal should be decomposed intoThe levels required for the reconstructionEEG recordings before WLD is performed2-8.5 Hz was found to be the mostOptimal frequency band for MMN in their dataset

Material and Methods

2.1 Experimental design and procedureExperimental designThe data were collected at the Department of Psychology at the University of Jyvskyl, FinlandMMN responses of 114 children without hearing defects were recordedThe mean age of the children was 11 years 8 months

Material and MethodsProcedureStep 1. The children listened to an uninterrupted soundAlternated between 100-ms sine tones of 600 Hz and 800 HzThere was no pause between the alternating tones and their amplitudes were equalStep 2. 15% of the 600-Hz tones were randomly replaced by shorter ones of 50-ms or 30-ms durationThe number of dev50ms was equal to that of dev30msMaterial and MethodsStep 3. There were at least six repetitions of alternating 100-ms tones between two deviants. The stimuli were presented binaurally through headphones at 65 dBStep 4. The children were instructed to not pay attention to the sounds While sitting quietly and still watching a silent movie for 15 minutesMaterial and Methods2.2EEG recordingsThe EEG recordings Were performed with Brain Atlas amplifiers with a 50K gainData acquisition of the EEG responses With a 12-bit 16-channel analog-to-digital converter(ADC)The down-sampling rate was 200 HzAnalog band-pass filter of 0.1-30 Hz was appliedThe data were processed offlineMaterial and Methods2.3Data reductionIn order to remove artifacts, two exclusion principles based on visual inspection were usedA trial in which recordingsEye movements exceeding were removed was conductedOnly a straight line with null information were removed was conducted

Material and Methods2.4Wavelet decomposition

The mathematical equations of the reverse biorthogonal wavelet N were derived by Daubechies

Material and Methods2.4.1 Determination of the number of levels for decomposition In WLDAn optimal decomposition with L levels is allowed under the condition:

Where N is the number of the samples of the decomposed signal Duration is less than one secondIn our study, the recordings had 130 samples (650 ms)The signal could be decomposed into seven levels

Material and MethodsThe roughly defined Bandwidth at a given level in WLD Related to the sampling frequency and the corresponding frequency levels as:

Where The sampling frequency in the experiment was set to 200 Hz for the data recordings

Material and Methods2.4.2 Selection of wavelet and number of levels for reconstructionThe procedure includes four steps:1)The unit impulse is decomposed into a few levels by a wavelet2)Each level is used for the reconstruction3)The Fourier transform of the reconstructed signal is performed To obtain the frequency responses at each level4) The appropriate wavelet and proper levels for the reconstruction of the desired signal

Material and MethodsAs indicated in Table 1The frequency ranges for D5 and D6 best matched the optimal frequency range of MMNHence, the coefficients for D5 and D6 should be chosen for reconstructing the desired MMN

Material and MethodsThe bandwidth at each level is shown in Table 1.

optimalMaterial and MethodsFigure 2 showsThe frequency ranges of the levels are different from those given in Table 1The magnitude responses are not as flat as those obtained using an optimal band-pass digital filterThe fifth and sixth levels are the optimal levels for reconstructing MMNMaterial and Methods

the optimal levelsMaterial and MethodsFor the filter, the stop band can be defined to be at the frequency whose gain is below -20 dBIn order to separate the responses of repeated stimuli and the MMNThe stop frequency should be around 8.5 HzThis is the first criterion for choosing a suitable waveletMaterial and MethodsThe selected wavelets had almost the same frequency at a 0-dB gainThe gain of the frequency responses at 0.1 Hz should be as low as possible to remove low-frequency driftTo make the final decision, the frequency responses of WLD for the two wavelets were calculated, respectivelyMaterial and MethodsFigure 6 shows The magnitudes of their frequency responses and that for the ODF

Daubechies wavelet with an order of 7 between 8.8 Hz and 10.8 Hz were larger than -20dB, so this wavelet was rejectedThe reverse biorthogonal wavelet with an order of 6.8 was chosen for the WLD of MMNMaterial and Methods2.5 Data processing methods for comparisonThe conventional average should be calculated first to reduce the computation loadThe DW, ODF, and WLD were performed on the averaged trace, respectively

Material and Methods2.6 Analyzing MMN peak measurementMMN measurements from the DWThe peak amplitude Latency were examinedThe MMN peak amplitude and latency were examined Using repeated measures analysis of variance (ANOVA) to determine Whether a difference of MMN measurements between the two deviants was evident under each method, respectivelyResultsFigure 7 showsgrand averaged waveforms obtainedprocedures for dev50m and dev30msUsing an conventional averageODFWLDResults

The trace from -350 ms to -50 ms is the standard sweep 0 ms to 300 ms is the deviant sweepSolid lines the WLDDashed linesODFDotted linesconventionally averaged tracesResults

The trace from -330 ms to -30 ms is the standard sweep0 ms to 300 ms is the deviant sweepResultsIn the standard sweep, WLD and the ODF effectively cancelled the responses to repeated stimuliIn contrast to the conventional averageIn the deviant sweep, WLD almost completely removed P3aIn contrast to the conventional average and ODF traces.

ResultsTable 2 showsStatistical test results of the MMN peak magnitude and latency for each method for the two deviantsFor ANOVA, the deviant for eliciting MMN was the factor, with the two deviants as the two levelsResults

significantlyResultsResults show That the proposed WLD performed differently with the ODF, the DW, or WLD-Coif in extracting MMN

32ConclusionsRegarding the application to mismatch negativity (MMN)The frequency response of WLD should Match the properties of MMN in time and frequency domainsFound that WLD with a reverse biorthogonal wavelet with an order of 6.8 Can contribute better properties of MMN, meeting its theoretical expectationsConclusionsThis study provides a novel procedureTo design an effective wavelet filter for reducing noiseInterference and sources of no interest in the research of event-related potentialsFound that the frequency response of a wavelet filterMaybe affected by the number of samples of the filtered signalThe sampling frequencyThe type of waveletsThe level of decompositionThank you for your attention