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![Page 1: Frequency-response-based Wavelet Decomposition for Extracting Children’s Mismatch Negativity Elicited by Uninterrupted Sound Department of Mathematical.](https://reader038.fdocuments.in/reader038/viewer/2022110406/56649f065503460f94c1bc2c/html5/thumbnails/1.jpg)
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
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Outline
IntroductionPurposesMaterials and MethodsResultsConclusions
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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)
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IntroductionFigure 1 Shows an oddball paradigm
the repeated standard stimuli
the deviant stimuli
The standard sweep
the deviant sweep
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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)
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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
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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
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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
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Material and Methods
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
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Material and Methods
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
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Material and Methods
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
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Material and Methods
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
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Material and Methods
2.4Wavelet decomposition
The mathematical equations of the reverse biorthogonal wavelet N were derived by Daubechies
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Material and Methods
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
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Material and Methods
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
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Material and Methods
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
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Material and Methods
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
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Material and Methods
The bandwidth at each level is shown in Table 1.
optimal
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Material and Methods
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
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Material and Methods
the optimal levels
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Material and Methods
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
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Material and Methods
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
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Material and Methods
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
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Material and Methods
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
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Material and Methods
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
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Results
Figure 7 showsgrand averaged waveforms obtained , procedures for
dev50m and dev30msUsing an conventional average
ODFWLD
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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
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Results
The trace from -330 ms to -30 ms is the standard sweep0 ms to 300 ms is the deviant sweep
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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.
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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
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Results significantly
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Results
Results show That the proposed WLD performed differently with the ODF,
the DW, or WLD-Coif in extracting MMN
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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
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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
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Thank you for your attention