The week's pick
Reseach Article
Artifact Removal from EEG using Spatially Constrained FastICA and Fuzzy Shrink Thresholding Technique
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 4 - Number 11 |
| Year of Publication: 2012 |
| Authors: G. Geetha, S. N. Geethalakshmi |
10.5120/ijais12-450814
|
G. Geetha, S. N. Geethalakshmi . Artifact Removal from EEG using Spatially Constrained FastICA and Fuzzy Shrink Thresholding Technique. International Journal of Applied Information Systems. 4, 11 ( December 2012), 25-29. DOI=10.5120/ijais12-450814
Abstract
This paper presents a novel technique for removing the artifacts from the Electroencephalogram (EEG) signals. EEG signals are influenced by different characteristics, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). The elimination of artifact from scalp EEGs is of substantial significance for both the automated and visual examination of underlying brainwave actions. These noise sources increase the difficulty in analysing the EEG and obtaining clinical information related to pathology. Hence it is crucial to design a procedure to decrease such artifacts in EEG records. This paper uses Spatially-Constrained Fast ICA (SC-FastICA) to separate the Independent Components (ICs) from the initial EEG signal. As the next step, Wavelet Denoising (WD) is applied to extract the brain activity from purged artifacts, where thresholding plays an important role in delineating the artifacts and hence a better thresholding technique called fuzzy Shrink thresholding is applied. Experimental results show that the proposed technique results in better removal of artifacts.
References
- Shi-Yun Shao, Kai-Quan Shen, Chong Jin Ong, Wilder-Smith, E and Xiao-Ping Li, "Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach with Error Correction", IEEE Transactions on Biomedical Engineering, Vol. 56, Pp. 336-344, 2009.
- Shi-Yun Shao, Kai-Quan Shen, Chong-Jin Ong, Xiao-Ping Li and Wilder-Smith, E. P. V, "Automatic identification and removal of artifacts in EEG using a probabilistic multi-class SVM approach with error correction", IEEE International Conference on Systems, Man and Cybernetics, Pp. 1134-1139, 2008.
- Kavitha, P. T, Lau, C. T and Premkumar, A. B. , "Modified ocular artifact removal technique from EEG by adaptive filtering", International Conference on Information, Communications & Signal Processing, Pp. 1-5, 2007.
- Kyung Hwan Kim, Hyo Woon Yoon and Hyun Wook Park, "Improved algorithm for ballistocardiac artifact removal from EEG simultaneously recorded with fMRI", 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 1, Pp. 936-939, 2004.
- P. LeVan, E. Urrestarrazu, and J. Gotman, "A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification", Clinical Neurophysiology, Vol. 117, No. 4, pp. 912- 927, 2006.
- R. J. Croft and R. J. Barry, "Removal of ocular artifact from the EEG: areview ", Clinical Neurophysiology, Vol. 30, No. 1, pp. 5 – 19, 2000.
- CA. Joyce, IF. Gorodnitsky, M. Kutas, "Automatic removal of eye movement and blink artifacts from EEG data using blind component separation ",Phychophysiology. , Vol. 41, No. 2, pp. 313- 325, 2004.
- 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, pp. 45-57, 2006.
- V. Krishnaveni, S. Jayaraman, N. Malmurugan, A. Kandasamy, D. Ramadoss, "Non adaptive thresholding methods for correcting ocularartifacts in EEG ", Academic Open Internet Journal, Vol. 13, 2004.
- Shlomit Yuval-Greenberg, Orr Tomer, Alon S. Keren, Israel Nelken and Leon Y. Deouell, "Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades", Neuron, Vol. 58, No. 3, pp. 429- 441, 2008.
- S. Verobyov and A. Cichocki. Blind noise reduction of multisensory signals using ICA and subspace filtering, with application to EEG analysis. Biological Cybernetics, 86:293-303, 2002.
- M. Potter, N. Gadhok, and W. Kinsner. Separation performance of ICA on simulated EEG and ECG signals contaminated by noise. Canadian Journal of Electrical and Computer Engineering, 27(3):123-127, July 2002.
- S. Choi, A. Cichocki, H. Park, S. Lee, blind Source "Separation and Independent Component Analysis : A Review", Neural Information Processing – Letters and Reviews, Vol. 6, no. 1, January 2005.
- A. Cichocki, Shun-ichi Amari, Adaptative blind Signal and Image Processing Learning Algorithms and Applications, John Wiley & Sons, ltd, 2002.
- Sutherland, M. T. , and Tang A. C. " Blind source separation can recover systematically distributed neuronal sources from "resting" EEG", Proceedings of the Second International Symposium on Communications, Control, and Signal Processing (ISCCSP 2006), Marrakech, Morocco, March 13-15.
- Joep J. M. Kierkels, Geert J. M. Van Botel, and Leo L. M. Vogten. 'A Model-Based Objective Evaluation of Eye Movement Correction in EEG Recordings", IEEE Transactions on biomedical engineering, vol. 53, No. 2, February 2006.
- Muhammad Tahir Akhtar and Christopher J. James, "Focal Artifact Removal from Ongoing EEG – A Hybrid Approach Based on Spatially-Constrained ICA and Wavelet De-noising", Annual International Conference of the IEEE EMBSMinneapolis, Pp. 4027-4030, 2009.
- N. P. Castellanos and V. A. Makarov, "Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent componentanalysis," J. Neuroscience Methods, vol. 158, pp. 300–312, 2006.
- G. Geetha , Dr. S. N. Geethalakshmi,"Artifact Removal from EEG using Spatially Constrained Independent Component Analysis and Wavelet Denoising with Otsu' Thresholding Technique," Elsevier Proceedings,2011.
- Jamal Saeedi, Mohammad Hassan Moradi, Karim Faez"A new wavelet-based fuzzy single and multi-channel image denoising",Elsevier Image and vision Computing,pp. 1611-1623,2010.
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