The week's pick
Reseach Article
A Certain Exploration on EEG Signal for the Removal of Artefacts using Power Spectral Density Analysis through Haar wavelet Transform
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 42 - Number 3 |
| Year of Publication: 2012 |
| Authors: B. Paulchamy, Ila Vennila |
10.5120/5671-7409
|
B. Paulchamy, Ila Vennila . A Certain Exploration on EEG Signal for the Removal of Artefacts using Power Spectral Density Analysis through Haar wavelet Transform. International Journal of Computer Applications. 42, 3 ( March 2012), 8-13. DOI=10.5120/5671-7409
Abstract
Electroencephalogram is the method of recording the electrical activity of the brain via sensors (electrodes) placed on the surface of the scalp. EEGs, which are of the order of microvolt, have greater potential for the diagnosis and treatment of mental and brain diseases and abnormalities. However, EEG signals are subject to various kinds of contaminants (i. e) artifacts which arise from human beings themselves. EEG recordings are often significantly distorted by Eye blinks and eye ball movements which cause changes to the electrical fields around the eye. These interpretations, which are often termed as noises, are however problematic. These are of the order of millivolts. The frequency range of EEG waves is 0-64Hz while artifacts occur within the range of 0-16Hz. Therefore, it becomes a must for the removal of ocular artifacts from the EEG signals. The wavelet based EOG algorithm, when applied to the entire length of the EEG signals, results in thresholding both high frequency and low frequency components, including the non-ocular artifacts zones, but it produces considerable loss in the valuable background EEG activity. The ocular artifacts can be detected by means of visual inspection. But, the EOG correction procedure requires ocular artifacts time zones to be fed as input , which is indeed a tedious process . This necessitates the need for the automatic detection of ocular artifacts zones. In this paper, we have proposed a method for automatically identifying the slow varying artifacts zones, followed by the application of wavelet based adaptive thresholding algorithm to the identified artifacts zones. This preserves the background EEG information. Adaptive thresholding is applied only to the artifacts zones. This method preserves both the low frequency components and the shape of the EEG signal in the non-artifact zones, which is very important in clinical diagnosis. In this paper, we have proposed a thresholding method using Haar Wavelet Transform for the removal of artifacts from the recorded EEG signal.
References
- R. J. Croft and R. J. Barry, "Removal of artifact from the EEG: a review ", Clinical Neurophysiology, Vol. 30, No. 1, (2000), pp. 5 – 19.
- P. Levin, E. Urrestarrazu, and J. Gotman, "A system for automatic artifact removal in octal scalp EEG based on independent component analysis and Bayesian classification", Clinical Neurophysiology, Vol. 117, No. 4, (2006), pp. 912- 927.
- CA. Joyce, IF. Gorodnitsky, M. Kutas, "Automatic removal of eye movement and blink Artifacts from EEG data using blind component separation ", Psychophysiology. Vol. 41, No. 2, (2004),
- V. Krishnaveni, S. Jayaraman, et. al, "Automatic identification and removal of Artefacts from EEG Using Wavelet transform ", Measurement Science review, Vol. 6, Section 2 No. 4. 2011.
- C. Sidney Burrus, Ramesh A Gopinath and Haitao Guo, "Introduction to Wavelets and Wavelet Transforms ", Prentice-Hall international Inc, (1998).
- 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.
- A. L. Goldberger,AmaralLAN,L. Glass,JM. Hausdorff,Ivanovich,RG. Mark,JE. Mietus,GB. MOODY,Ck. Peng,HE. Stanley,PhysioToolkitand Physionet:Components of a New Reasearch Resource for Complex Physiologic Signals Circulation101(23):e215-e220CirculationElectronicWeb Page httpcic. ahajournals. org/cgi/content/full/101/23/e215:2000
- V. Krishnaveni, S. Jayaraman, N. Malmurugan, A. Kandasamy, D. Ramadoss, "Non adaptive thresholding methods for correcting Artifacts in EEG ", Academic Open Internet Journal, Vol. 13, (2004).
- Aboitiz, F. , Scheibel, A. B. , Fisher, R. S. , &Zaidel, E. (1992). Individual differences in brain asymmetries and fiber composition in the human corpus callosum. Brain Research, 598, 154-161.
- Barlow, J. S. (1986). Artifact processing (rejection and minimization) in EEG data processing. In F. H. Lopes da Silva, W. S. van Leeuwen, & A. Remond (Eds. ), Handbook of EEG and Clinical Neurophysiological Signals Vol. 2, 15-62.
- Barry, R. J. (1976). Failure to find the "local" EEG OR to low- level auditory stimulation. Physiological Psychology, 4, 171-174.
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