Selection of a Suitable Wavelet for Cognitive Memory Using Electroencephalograph Signal

DOI: 10.4236/eng.2013.55B004   PDF   HTML     3,963 Downloads   5,307 Views   Citations


The aim of this study is to recognize the best and suitable wavelet family for analyzing cognitive memory using Electroencephalograph (EEG) signal. The participant was given some visual stimuli during the study phase, which were a sequence of pictures that had to be remembered to acquire the EEG signal. The Neurofax EEG 9200 was used to record the acquisition of cognitive memory at channel Fz. The raw EEG signals were analyzed using Wavelet Transform. A lot of mother wavelets can be used for analyzing the signal, but do not lose any information on the wavelet, some predictions must be made beforehand. The criteria of the EEG signal were narrowed down to the Daubechies, Symlets and Coiflets, and it is the final selection depending on their Mean Square Error (MSE). The best solution would have the least difference between the original and constructed signal. Results indicated that the Daubechies wavelet at a level of decomposition of 4 (db4) was the most suitable wavelet for pre-processing the raw EEG signal of cognitive memory. To conclude, choosing the suitable wavelet family is more important than relying on the MSE value alone to successfully perform a wavelet transformation.

Share and Cite:

S. Z. M. Tumari, R. Sudirman and A. H. Ahmad, "Selection of a Suitable Wavelet for Cognitive Memory Using Electroencephalograph Signal," Engineering, Vol. 5 No. 5B, 2013, pp. 15-19. doi: 10.4236/eng.2013.55B004.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] G. Neale and K. Tehan, “Age and Redintegration in Immediate Memory and Their Relationship to Task Difficulty,” Memory and Cognition, Vol. 8, No. 35, 2007, pp. 1940-1953. doi:10.3758/BF03192927
[2] N. Unsworth and R. W. Engle, “Simple and Complex Memory Spans and Their Relation to Fluid Abilities: Evidence from List-Length Effects,” Journal of Memory and Language, Vol. 54, No. 1, 2006, pp. 68-80. doi:10.1016/j.jml.2005.06.003
[3] S. Lewandowsky, S. M. Geiger and D. B. Morrell, “Turning Simple Span into Complex Span: Time for Decay or Interference from Distractors?” in Simple and Complex Span, Australia , 2007, pp. 1-71.
[4] H. Adeli, Z. Zhou and N. Dadmehr, “Analysis of EEG Records in an Epileptic Patient using Wavelet Transform.,” Journal of Neuroscience Methods, Vol. 123, No. 1, 2003, pp. 69-87. doi:10.1016/S0165-0270(02)00340-0
[5] A. Roth, D. Roesch-Ely, S. Bender, M. Weisbrod and S. Kaiser, “Increased Event-Related Potential Latency and Amplitude Variability in Schizophrenia Detected Through Wavelet-based Single Trial Analysis,” Journal of the International Organization of Psychophysiology, Vol. 66, No. 3, 2007, pp. 244-254. doi:10.1016/j.ijpsycho.2007.08.005
[6] J. M. Misiti, M. Misiti, Y. Oppenhum and G. Poggi, “Wavelet Toolbox: For Use With MATLAB,” 1st ed, The Mathworks, Incorporation, 1996, pp. 1-626.
[7] A. I. Megahed, A. Monem Moussa, H. B. Elrefaie and Y. M. Marghany, “Selection of a Suitable Mother Wavelet for Analyzing Power System Fault Transients,” 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008, pp. 1-7.
[8] C. Bowman and A. C. Newell, “A Wavelet based Algorithm for Pattern Analysis,” Journal of Physica D, vol. 119, pp. 250-282, 1998. doi:10.1016/S0167-2789(98)00039-6
[9] S. Lee, W.-S. Kang and K. Cho, “A Method of Mother Wavelet Function Learning for DWT -based Analysis using EEG Signals 2,” IEEE, 2011, pp. 2-5.
[10] H. Ocak, “Automatic Detection of Epileptic Seizures in EEG using Discrete Wavelet Transform and Approximate Entropy,” Expert Systems with Applications, Vol. 36, No. 2, 2009, pp. 2027-2036. doi:10.1016/j.eswa.2007.12.065
[11] D. Sripathi, “CHAPTER 2: The Discrete Wavelet Transform,” 2003, pp. 6-15.
[12] M. O. Oliveira and A. S. Bretas, “Application of Discrete Wavelet Transform for Differential Protection of Power Transformers,” in Discrete Wavelet Transforms - Biomedical Applications, H. Oikkonen, Ed. Shanghai: InTech, 2008, pp. 349-367.
[13] R. Polikar, “The Story of Wavelets 1,” in Physics and Modern Topics in Mechanical and Electrical Engineering, USA: Press, World Scientific and Eng, Society, 1999, pp. 192-197.
[14] A. C. Merzagora, S. Bunce, M. Izzetoglu and B. Onaral, “Wavelet Analysis for EEG Feature Extraction in Deception Detection.,” IEEE Engineering in Medicine and Biology Society Conference, 2006, Vol. 1, pp. 2434-2437.
[15] M. Antonini, “Mean Square Error Approximation for Wavelet-Based Semiregular Mesh Compression,” Vol. 12, No. 4, pp. 649-657, 2006.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.