frequency-response-based wavelet decomposition for extracting children’s mismatch negativity...

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,ChinaDepartment of Psychology,University of Jyväskylä, Jyväskylä 40014,Finland

Received 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 stimuli

the deviant stimuli

The standard sweep

the deviant sweep

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 techniquesDigital filtersWavelet decomposition (WLD)Principal component analysis(PCA)Independent component analysis(ICA)

Introduction

Wavelet 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 filter

Purposes

Designs 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 most

Optimal 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 Jyväskylä, FinlandMMN responses of 114 children without hearing defects were

recordedThe mean age of the children was 11 years 8 months


ProcedureStep 1. The children listened to an uninterrupted sound

Alternated 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 dev30ms


Step 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

minutes


2.2EEG 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 offline


2.3Data reductionIn order to remove artifacts, two exclusion principles

based on visual inspection were usedA trial in which recordings

Eye movements exceeding were removed was conductedOnly a straight line with null information were removed

was conducted

100 V


2.4Wavelet decomposition

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


2.4.1 Determination of the number of levels for decomposition In WLD

An 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

7L

2LN


The 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

1/ 2lB F

1,...,l L


2.4.2 Selection of wavelet and number of levels for reconstruction

The procedure includes four steps:1)The unit impulse is decomposed into a few levels by a wavelet

2)Each level is used for the reconstruction

3)The Fourier transform of the reconstructed signal is performed To obtain the frequency responses at each level

4) The appropriate wavelet and proper levels for the

reconstruction of the desired signal


As 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


The bandwidth at each level is shown in Table 1.

optimal


Figure 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 MMN


the optimal levels


For the filter, the stop band can be defined to be at the frequency whose gain is below -20 dB

In order to separate the responses of repeated stimuli and the MMNThe stop frequency should be around 8.5 Hz

This is the first criterion for choosing a suitable wavelet


The 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, respectively


Figure 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 rejected

The reverse biorthogonal wavelet with an order of 6.8 was chosen for the WLD of MMN


2.5 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


2.6 Analyzing MMN peak measurementMMN measurements from the DW

The peak amplitude Latency were examined

The 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, respectively

Results

Figure 7 showsgrand averaged waveforms obtained ， procedures for

dev50m and dev30msUsing an conventional average

ODFWLD

Results

The trace from -350 ms to -50 ms is the standard sweep 0 ms to 300 ms is the deviant sweep

Solid lines ： the WLDDashed lines ： ODFDotted lines ： conventionally averaged traces

Results

The trace from -330 ms to -30 ms is the standard sweep0 ms to 300 ms is the deviant sweep

Results

In the standard sweep, WLD and the ODF effectively cancelled the responses to repeated stimuliIn contrast to the conventional average

In the deviant sweep, WLD almost completely removed P3aIn contrast to the conventional average and ODF traces.

Results

Table 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 levels

Results significantly

Results

Results show That the proposed WLD performed differently with the ODF,

the DW, or WLD-Coif in extracting MMN

Conclusions

Regarding the application to mismatch negativity (MMN)The frequency response of WLD should

Match the properties of MMN in time and frequency domains

Found that WLD with a reverse biorthogonal wavelet with an order of 6.8 Can contribute better properties of MMN, meeting its

theoretical expectations

Conclusions

This 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 filter

Maybe affected by the number of samples of the filtered signal

The sampling frequencyThe type of waveletsThe level of decomposition

Thank you for your attention

frequency-response-based wavelet decomposition for extracting children’s mismatch negativity...

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