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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
Event-related potentials (ERPs)
Applied to study the automatic auditory brain functions related
to discrimination
Perception in the brain of children with delayed language
development
An ERP component, called mismatch negativity (MMN)
Introduction
Figure 1
Shows an oddball paradigm
the deviant stimuli
the repeated standard 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 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
Material and Methods
2.1 Experimental design and procedure
Experimental design
The data were collected at the Department of Psychology at
the University of Jyväskylä, Finland
MMN responses of 114 children without hearing defects were
recorded
The mean age of the children was 11 years 8 months
Material and Methods
Procedure
Step 1. The children listened to an uninterrupted sound
Alternated between 100-ms sine tones of 600 Hz and 800 Hz
There was no pause between the alternating tones and their
amplitudes were equal
Step 2. 15% of the 600-Hz tones were randomly replaced by
shorter ones of 50-ms or 30-ms duration
The number of dev50ms was equal to that of dev30ms
Material and Methods
Step 3. There were at least six repetitions of alternating 100ms tones between two deviants.
The stimuli were presented binaurally through headphones at
65 dB
Step 4. The children were instructed to not pay attention to
the sounds
While sitting quietly and still watching a silent movie for 15
minutes
Material and Methods
2.2EEG recordings
The EEG recordings
Were performed with Brain Atlas amplifiers with a 50K gain
Data acquisition of the EEG responses
With a 12-bit 16-channel analog-to-digital converter(ADC)
The down-sampling rate was 200 Hz
Analog band-pass filter of 0.1-30 Hz was applied
The data were processed offline
Material and Methods
2.3Data reduction
In order to remove artifacts, two exclusion principles
based on visual inspection were used
A trial in which recordings
Eye movements exceeding 100V were removed was conducted
Only a straight line with null information were removed
was conducted
Material and Methods
2.4Wavelet decomposition
The mathematical equations of the reverse biorthogonal
wavelet N were derived by Daubechies
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:
N 2
L
Where N is the number of the samples of the decomposed
signal
L7
Duration is less than one second
In our study, the recordings had 130 samples (650 ms)
The signal could be decomposed into seven levels
Material and Methods
The roughly defined
Bandwidth at a given level in WLD
Related to the sampling frequency and the corresponding
frequency levels as:
B F / 2l 1
Where l 1,..., L
The sampling frequency in the experiment was set to
200 Hz for the data recordings
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
Material and Methods
As indicated in Table 1
The frequency ranges for ‘D5’ and ‘D6’ best matched the
optimal frequency range of MMN
Hence, the coefficients for ‘D5’ and ‘D6’ should be chosen
for reconstructing the desired MMN
Material and Methods
The bandwidth at each level is shown in Table 1.
optimal
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
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
MMN
The stop frequency should be around 8.5 Hz
This is the first criterion for choosing a suitable wavelet
Material and Methods
The selected wavelets had almost the same frequency at a 0dB gain
The gain of the frequency responses at 0.1 Hz should be as
low as possible to remove low-frequency drift
To make the final decision, the frequency responses of WLD
for the two wavelets were calculated, respectively
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
Material and Methods
2.5 Data processing methods for comparison
The conventional average should be calculated first to
reduce the computation load
The DW, ODF, and WLD were performed on the averaged
trace, respectively
Material and Methods
2.6 Analyzing MMN peak measurement
MMN 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 shows
grand averaged waveforms obtained,procedures for
dev50m and dev30ms
Using an conventional average
ODF
WLD
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 WLD
Dashed lines:ODF
Dotted lines:conventionally
averaged traces
Results
The trace from -330 ms to -30
ms is the standard sweep
0 ms to 300 ms is the deviant
sweep
Results
In the standard sweep, WLD and the ODF effectively
cancelled the responses to repeated stimuli
In contrast to the conventional average
In the deviant sweep, WLD almost completely removed P3a
In contrast to the conventional average and ODF traces.
Results
Table 2 shows
Statistical test results of the MMN peak magnitude and
latency for each method for the two deviants
For 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 procedure
To design an effective wavelet filter for reducing noise
Interference and sources of no interest in the research of eventrelated potentials
Found that the frequency response of a wavelet filter
Maybe affected by the number of samples of the filtered signal
The sampling frequency
The type of wavelets
The level of decomposition
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