Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks
Journal of Electrical and Electronic Engineering
Volume 3, Issue 2-1, March 2015, Pages: 21-24
Received: Nov. 16, 2014; Accepted: Nov. 19, 2014; Published: Nov. 24, 2014
Views 2969      Downloads 166
Authors
Mohammad Seifi, Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran
Aliakbar Kargaran Erdechi, Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran
Ahmad Hajipour, Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran
Article Tools
Follow on us
Abstract
Electroencephalogram (EEG) is one‎ of the commonly used non-invasive techniques for understanding the brain functions. This paper presents a method for removing electroocular (EOG) artifacts in the electroencephalogram (EEG). A new adaptive radial-basis function- networks- (RBFN-) for the adaptive noise cancellation (ANC) problem is proposed. Also, the algorithm of structure identification and parameters adjustment is developed. The proposed RBFN approach implements Takagi-Sugeno-Kang (TSK) fuzzy systems, functionally. Simulation results demonstrate that the proposed adaptive RBFN can remove the noise successfully and efficiently with a parsimonious structure.
Keywords
RBFN, ANC, Adaptive Filtering, Neural Network
To cite this article
Mohammad Seifi, Aliakbar Kargaran Erdechi, Ahmad Hajipour, Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks, Journal of Electrical and Electronic Engineering. Special Issue: Research and Practices in Electrical and Electronic Engineering in Developing Countries. Vol. 3, No. 2-1, 2015, pp. 21-24. doi: 10.11648/j.jeee.s.2015030201.15
References
[1]
R. Jonathan, J. M. Dennis, B. Niels, P. Gert and M. Theresa, “Brain computer interfaces for communication and control,” pp.767-791, Clinical Neurophysiology, 2002.
[2]
M. Fatourechi, A. Bashashati, K. W. Rahab and E. B. Gary, “EMG and EOG artifacts from BCI systems: a Survey,” pp. 480-494, Clinical Neurophysiology, 2007.
[3]
P.He, M.Kahle, G.Wilson and C.Russel, “Removal of ocular artifacts from EEG: A comparison of Adaptive filtering method and regression method using simulated data”, IEEE engineering in medicine and Biology 27th annual conference, 2005.
[4]
Ehsan Zezhadaraya, Mohammed B Shamosollahi. ” EOG artifact removal from EEG using ICA and ARMAX modeling”, Private communication, 2004.
[5]
P. He, G.Wilson and C.Russel, ” Removal of Ocular artifacts from Electro encephalogram by adaptive Filtering,” pp.407-412, Medical &Biological Engineering & Computing, 2004.
[6]
B.W.Jervis, M.Thomlinson, C.Mair, J.M. Lopez and M.I.B Garcia, ”Residual ocular artifacts subsequent to ocular artifact removal from the electroencephalogram”, pp.293-298, IEE Proceedings-Volume 146, Issue 6, Nov. 1999.
[7]
T. D. Lagerlund, F. W. Sharbrough, and N. E. Busacker, “Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition,” J. Clin. Neurophysiol., vol. 14, pp. 73– 82, 1997.
[8]
R. Verleger, T. Gasser, & J. Möcks, “Correction of EOG artifacts in eventrelated potentials of the EEG: Aspects of reliability and validity”, Psychophysiology, vol. 19, pp 472-480, 1982.
[9]
J. L. Whitton, F. Lue, & H. Moldofsky, “A spectral method for removing eye movement artifacts from the EEG”, Electroenceph. Clin. Neurophysiol. vol. 44, pp. 735-741, 1978.
[10]
J.S.R. Jaug, “Anfis: Adaptivenetwork-based fuzzy inference system,” IEEE Trans. on Systems,Man,and Cybernetics, vol. 23, pp. 665-685, 1993.
[11]
www.physionet.org
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186