We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

EEG De-noising using SURE Thresholding based on Wavelet Transforms

by G. Geetha, Dr.S.N.Geethalakshmi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 24 - Number 6
Year of Publication: 2011
Authors: G. Geetha, Dr.S.N.Geethalakshmi
10.5120/2948-3935

G. Geetha, Dr.S.N.Geethalakshmi . EEG De-noising using SURE Thresholding based on Wavelet Transforms. International Journal of Computer Applications. 24, 6 ( June 2011), 29-33. DOI=10.5120/2948-3935

@article{ 10.5120/2948-3935,
author = { G. Geetha, Dr.S.N.Geethalakshmi },
title = { EEG De-noising using SURE Thresholding based on Wavelet Transforms },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 24 },
number = { 6 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume24/number6/2948-3935/ },
doi = { 10.5120/2948-3935 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:18.401056+05:30
%A G. Geetha
%A Dr.S.N.Geethalakshmi
%T EEG De-noising using SURE Thresholding based on Wavelet Transforms
%J International Journal of Computer Applications
%@ 0975-8887
%V 24
%N 6
%P 29-33
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. However, the presence of artifacts like electro-oculogram (EOG), Electrocardiogram (ECG), electromyogram (EMG) and power-line noise in the EEG signal is a major problem in the study of brain potentials. Hence, these superfluous signals are needed to be removed. There are various methods for removal of artifacts. This paper discusses a wavelet-based approach for correcting the artifacts generated by eye blinks, eyeball movements and facial muscle movements in EEG.

References
  1. R R Coifman and D L Donoho, ‘Translation Invariant De-noising’, in A Antoniadis (editor): Wavelets and Statistics, Springer Verlag, 1995.
  2. C Sidney Burrus, R A Gopinath, and Haitao Guo, ‘Introduction to Wavelets and Wavelet Transforms: A Primer’, Prentice Hall, NJ, 1998
  3. V. J. Samar, Ajit Bopardikar, Raghuveer Rao, and Kenneth Swartz, “Wavelet analysis of neuroelectric waveforms: A conceptual tutorial “, Brain and Language, 66, (1999), pp. 7 – 60
  4. R.J. Croft and R.J. Barry, “Removal of ocular artifact from the EEG: a review “, Clinical Neurophysiology, Vol. 30, No.1, (2000), pp. 5 – 19.
  5. V. Krishnaveni, S. Jayaraman, S. Aravind, V. Hariharasudhan, K. Ramadoss, “Automatic identification and Removal of ocular artifacts from EEG using Wavelet transform “, Measurement Science Review, Vol. 6, No.4, (2006), pp.45-57.
  6. P. Senthilkumaret al,” Removal of Ocular Artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel”, Int. J. Open Problems Compt. Math., Vol. 1, No. 3, December 2008, pp.188-200.
  7. M. Bedeeuzzaman, O. Farooq and Y. U. Khan, “EEG signal classification for seizure analysis using statistical measures”, Proc. EIP 2009, Vol.1, Pp. 366-372, 2009.
  8. T. Gandhi, B. K. Panigrahi, M. Bhatia and S. Anand, “Expert model for detecton of epileptic activity in EEG signature”, Expert Systems with Applications, Vol. 37, No. 4, Pp. 3513-3520, 2010.
  9. E. D., Übeyli, “Combined neural network model employing wavelet coefficients for EEG signals clasification”, Digital Signal Processing, Vol. 19, Pp. 297–308, 2009.
  10. A. M. Lima, A. L. V. Coelho, S. Chagas, "Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines," Expert Systems with Applications, Vol. 36, Pp. 10054-10059, 2009.
  11. Mukul, M.K. and Matsuno, F., "EEG de-noising based on wavelet-transforms and Extraction of Sub-band Components related to movement imagination", ICCAS-SICE, Pp. 1605-1610, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Wavelets Ocular Artifacts (OA) Muscular Artifacts (MA)

Powered by PhDFocusTM