Chairman:Hung -Chi Yang Presenter: Yu-Kai Wang Advisor: Dr. Yeou-Jiunn Chen Date: 2013.3.6

1. 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 YangPresenter: Yu-Kai Wang Advisor: Dr. Yeou-Jiunn ChenDate: 2013.3.6

2. Outline • Introduction • Purposes • Materials and Methods • Results • Conclusions

3. 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)

4. Introduction • Figure 1 • Shows an oddball paradigm the deviant stimuli the repeated standard stimuli The standard sweep the deviant sweep

5. 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)

6. 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

7. 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

8. 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 8months

9. 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

10. 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 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

11. 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

12. 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 were removed was conducted • Only a straight line with null information were removed was conducted

13. Material and Methods • 2.4Wavelet decomposition • The mathematical equations of the reverse biorthogonal wavelet N were derived by Daubechies

14. 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 second • In our study, the recordings had 130 samples (650 ms) • The signal could be decomposed into seven levels

15. 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

16. 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

17. 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

18. Material and Methods • The bandwidth at each level is shown in Table 1. optimal

19. 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

20. Material and Methods the optimal levels

21. 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

22. Material and Methods • The selected wavelets had almost the same frequency at a 0-dB 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

23. 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

24. 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

25. 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

26. Results • Figure 7 shows • grand averaged waveforms obtained，procedures for dev50m and dev30ms • Using an conventional average • ODF • WLD

27. 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

28. Results The trace from -330 ms to -30 ms is the standard sweep 0 ms to 300 ms is the deviant sweep

29. 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.

30. 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

31. Results significantly

32. Results • Results show • That the proposed WLD performed differently with the ODF, the DW, or WLD-Coif in extracting MMN

33. 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

34. 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 event-related 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

35. Thank you for your attention