S. Z. M. TUMARI ET AL. 19
(c)
(d)
Figure 5. (a) Beta (b) Alpha (c) Theta (d) Delta: Frequency
Domain of EEG Signal for First Subject at Channel Fz.
Table 3. Description on parameter extraction of eeg signal
for study phase: first subjec t.
Beta
(D5) Alpha
(D6) Theta
(D7) Delta
(A7)
Mean 0.0289 0.3619 1.091 - 2.559
Standard Deviation 368.7 509.5 747.4 1183
Max Value 1703 2372 2054 2888
Median 146.3 320.5 438.3 856.9
PSD (µV) 5.82 0.101 0.013 0.011
Frequency (Hz) 9.77 5.86 1.95 0.98
for extracting the EEG signals into different frequency
bands.
6. Acknowledgements
Our appreciation also goes to the Malaysia Ministry of
Education, Johor Education Department, Zamalah Schol-
arship and Universiti Teknologi Malaysia for permission,
facilities and funding this project under QJ130000.
2623.09J28.
REFERENCES
[1] G. Neale and K. Tehan, “Age and Redintegration in Im-
mediate Memory and Their Relationship to Task Diffi-
culty,” 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 De-
cay 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 Trans-
form.,” 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. Mone m 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 - Con-
version and Delivery of Electrical Energy in the 21st
Century, 2008, pp. 1-7.
[8] C. Bowman and A. C. Newell, “A Wavelet based Algo-
rithm 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 us-
ing 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 Trans-
form,” 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 - Bio-
medical Applications, H. Oikkonen, Ed. Shanghai: In-
Tech, 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 Decep-
tion Detection.,” IEEE Engineering in Medicine and Bi-
ology 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.
Copyright © 2013 SciRes. ENG