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A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wavelet and ICA
Janett Walters-Williams, Yan Li
Pages - 130 - 148     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
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KEYWORDS
Independent Component Analysis, Wavelet Transform, Electroencephalogram (EEG), Unscented Kalman Filter, Cycle Spinning
ABSTRACT
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
CITED BY (9)  
1 princy, r., thamarai, p., & karthik, B. Denoising EEG Signal Using Wavelet Transform.
2 Sheoran, M., Kumar, S., & Chawla, S. (2015). Methods of denoising of electroencephalogram signal: a review. International Journal of Biomedical Engineering and Technology, 18(4), 385-395.
3 Al-Qazzaz, N. K., Hamid Bin Mohd Ali, S., Ahmad, S. A., Islam, M. S., & Escudero, J. (2015). Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task. Sensors, 15(11), 29015-29035.
4 Li, T., Wen, P., & Jayamaha, S. (2014). Anaesthetic EEG signal denoise using improved nonlocal mean methods. Australasian Physical & Engineering Sciences in Medicine, 37(2), 431-437.
5 Teng, C., Zhang, Y., & Wang, G. (2014, August). The removal of EMG artifact from EEG signals by the multivariate empirical mode decomposition. In Signal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference on (pp. 873-876). IEEE.
6 Al-Qazzaz, N. K., Ali, S. H. B., Ahmad, S. A., Chellappan, K., Islam, M. S., & Escudero, J. (2014). Role of EEG as Biomarker in the Early Detection and Classification of Dementia. The Scientific World Journal, 2014.
7 Prinza, L., Mohanalin, J., Beenamol, M., & Joshy, P. V. (2014). Denoising Performance of Complex Wavelet Transform with Shannon Entropy and its Impact on Alzheimer Disease EEG Classification Using Neural Network. Journal of Medical Imaging and Health Informatics, 4(2), 186-196.
8 Khatwani, P., & Tiwari, A. (2013). A survey on different noise removal techniques of EEG signals. International Journal of Advanced Research in Computer and Communication Engineering, 2(2), 1091-1095.
9 Roy, V., & Shukla, S. (2013, January). Image denoising by data adaptive and non-data adaptive transform domain denoising method using Eeg signal. In Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012) (pp. 9-20). Springer India.
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Mr. Janett Walters-Williams
- Jamaica
jwalters@utech.edu.jm
Dr. Yan Li
Department of Mathematics & Computing, Faculty of Sciences, University of Southern Queensland, Toowoomba, Australia - Australia