Hilbert-Huang versus Morlet wavelet transformation on mismatch negativity of children in uninterrupted sound paradigm

Background Compared to the waveform or spectrum analysis of event-related potentials (ERPs), time-frequency representation (TFR) has the advantage of revealing the ERPs time and frequency domain information simultaneously. As the human brain could be modeled as a complicated nonlinear system, it is interesting from the view of psychological knowledge to study the performance of the nonlinear and linear time-frequency representation methods for ERP research. In this study Hilbert-Huang transformation (HHT) and Morlet wavelet transformation (MWT) were performed on mismatch negativity (MMN) of children. Participants were 102 children aged 8–16 years. MMN was elicited in a passive oddball paradigm with duration deviants. The stimuli consisted of an uninterrupted sound including two alternating 100 ms tones (600 and 800 Hz) with infrequent 50 ms or 30 ms 600 Hz deviant tones. In theory larger deviant should elicit larger MMN. This theoretical expectation is used as a criterion to test two TFR methods in this study. For statistical analysis MMN support to absence ratio (SAR) could be utilized to qualify TFR of MMN. Results Compared to MWT, the TFR of MMN with HHT was much sharper, sparser, and clearer. Statistically, SAR showed significant difference between the MMNs elicited by two deviants with HHT but not with MWT, and the larger deviant elicited MMN with larger SAR. Conclusion Support to absence ratio of Hilbert-Huang Transformation on mismatch negativity meets the theoretical expectations, i.e., the more deviant stimulus elicits larger MMN. However, Morlet wavelet transformation does not reveal that. Thus, HHT seems more appropriate in analyzing event-related potentials in the time-frequency domain. HHT appears to evaluate ERPs more accurately and provide theoretically valid information of the brain responses.

[1]  Thalía Harmony.  Neurometric Assessment of Brain Dysfunction in Neurological Patients , 1984 .

[2]  R. Näätänen.  Attention and brain function , 1992 .

[3]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[4]  F. L. D. Silva,et al.  EEG analysis: Theory and Practice , 1998 .

[5]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[7]  Margot J. Taylor,et al.  Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. , 2000, Psychophysiology.

[8]  Eckehard Olbrich,et al.  Oscillatory events in the human sleep EEG - detection and properties , 2004, Neurocomputing.

[9]  Hualou Liang,et al.  Empirical mode decomposition: a method for analyzing neural data , 2005, Neurocomputing.

[10]  H. Liang,et al.  Artifact reduction in electrogastrogram based on empirical mode decomposition method , 2006, Medical and Biological Engineering and Computing.

[11]  Xiaoli Li.  Temporal structure of neuronal population oscillations with empirical model decomposition , 2006 .

[12]  J. Sleigh,et al.  Measure of the electroencephalographic effects of sevoflurane using recurrence dynamics , 2007, Neuroscience Letters.

[13]  I. Winkler.  Interpreting the Mismatch Negativity , 2007 .

[14]  R. Näätänen,et al.  Measurement of extensive auditory discrimination profiles using the mismatch negativity (MMN) of the auditory event-related potential (ERP) , 2007, Clinical Neurophysiology.

[15]  H. Lyytinen,et al.  Does Mismatch Negativity Show Differences in Reading-Disabled Children Compared to Normal Children and Children with Attention Deficit? , 2007, Developmental neuropsychology.

[16]  J. Joutsensalo,et al.  Optimal Digital Filtering versus Difference Waves on the Mismatch Negativity in an Uninterrupted Sound Paradigm , 2007, Developmental neuropsychology.

[17]  Fengyu Cong,et al.  ERP qualification exploiting waveform, spectral and time-frequency infomax , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[18]  H. Lyytinen,et al.  Independent component analysis on the mismatch negativity in an uninterrupted sound paradigm , 2008, Journal of Neuroscience Methods.

[19]  Wlodzimierz Klonowski.  Importance of Nonlinear Signal Processing in Biomedicine , 2008 .

[20]  H. Lyytinen,et al.  Mismatch negativity (MMN) elicited by duration deviations in children with reading disorder, attention deficit or both. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.