Spectral entropy of dyslexic ERP signal by means of Adaptive Optimal Kernel

In this paper, subband spectral entropy (SSE) and its relative form was used for the analysis of rest electroencephalogram (EEG) and Event Related Potentials (ERP). The recorded signals were taken from control children and children with dyslexia. Adaptive-Optimal-Kernel (AOK) time-frequency representation was used to produce high resolution spectrogram. Then, SSE and relative subband spectral entropy (RSSE) were calculated. The entropic patterns of both controls and dyslexics were investigated showing differences in specific electrode recordings.

[1]  Konstantina S. Nikita,et al.  Wavelet entropy differentiations of event related potentials in dyslexia , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[2]  A. Keil,et al.  Large-scale neural correlates of developmental dyslexia , 2004, European Child & Adolescent Psychiatry.

[3]  M. Eckert.  Neuroanatomical Markers for Dyslexia: A Review of Dyslexia Structural Imaging Studies , 2004, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[4]  Douglas L. Jones,et al.  An adaptive optimal-kernel time-frequency representation , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  Douglas L. Jones,et al.  A radially-Gaussian, signal-dependent time-frequency representation , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[6]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[7]  R Quian Quiroga,et al.  Wavelet entropy: a measure of order in evoked potentials. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[8]  T. Inouye,et al.  Quantification of EEG irregularity by use of the entropy of the power spectrum. , 1991, Electroencephalography and clinical neurophysiology.

[9]  Osvaldo A. Rosso,et al.  Wavelet entropy in event-related potentials: a new method shows ordering of EEG oscillations , 2001, Biological Cybernetics.

[10]  E. Basar,et al.  Brain oscillations in perception and memory. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[11]  E. Basar,et al.  Wavelet entropy: a new tool for analysis of short duration brain electrical signals , 2001, Journal of Neuroscience Methods.

[12]  Michael Zouberakis,et al.  8th IEEE International Conference on BioInformatics and BioEngineering, 2008. BIBE 2008 , 2008 .

[13]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[14]  B. Shaywitz,et al.  Dyslexia (specific reading disability). , 2003, Pediatrics in review.

[15]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Sacha Jennifer van Albada,et al.  Transformation of arbitrary distributions to the normal distribution with application to EEG test–retest reliability , 2007, Journal of Neuroscience Methods.