Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks

Mohammad Seifi; Aliakbar Kargaran Erdechi; Ahmad Hajipour

doi:doi:10.11648/j.jeee.s.2015030201.15

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Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks

Mohammad Seifi, Aliakbar Kargaran Erdechi, Ahmad Hajipour

Published in Journal of Electrical and Electronic Engineering (Volume 3, Issue 2-1)

Received: 16 November 2014 Accepted: 19 November 2014 Published: 24 November 2014

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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.

Published in	Journal of Electrical and Electronic Engineering (Volume 3, Issue 2-1) This article belongs to the Special Issue Research and Practices in Electrical and Electronic Engineering in Developing Countries
DOI	10.11648/j.jeee.s.2015030201.15
Page(s)	21-24
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

RBFN, ANC, Adaptive Filtering, Neural Network

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

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APA Style

Mohammad Seifi, Aliakbar Kargaran Erdechi, Ahmad Hajipour. (2014). Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks. Journal of Electrical and Electronic Engineering, 3(2-1), 21-24. https://doi.org/10.11648/j.jeee.s.2015030201.15

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ACS Style

Mohammad Seifi; Aliakbar Kargaran Erdechi; Ahmad Hajipour. Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks. J. Electr. Electron. Eng. 2014, 3(2-1), 21-24. doi: 10.11648/j.jeee.s.2015030201.15

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AMA Style

Mohammad Seifi, Aliakbar Kargaran Erdechi, Ahmad Hajipour. Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks. J Electr Electron Eng. 2014;3(2-1):21-24. doi: 10.11648/j.jeee.s.2015030201.15

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@article{10.11648/j.jeee.s.2015030201.15,
  author = {Mohammad Seifi and Aliakbar Kargaran Erdechi and Ahmad Hajipour},
  title = {Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {3},
  number = {2-1},
  pages = {21-24},
  doi = {10.11648/j.jeee.s.2015030201.15},
  url = {https://doi.org/10.11648/j.jeee.s.2015030201.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.s.2015030201.15},
  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.},
 year = {2014}
}

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TY  - JOUR
T1  - Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks
AU  - Mohammad Seifi
AU  - Aliakbar Kargaran Erdechi
AU  - Ahmad Hajipour
Y1  - 2014/11/24
PY  - 2014
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DO  - 10.11648/j.jeee.s.2015030201.15
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 21
EP  - 24
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.s.2015030201.15
AB  - 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.
VL  - 3
IS  - 2-1
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Mohammad Seifi, Aliakbar Kargaran Erdechi, Ahmad Hajipour. (2014). Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks. Journal of Electrical and Electronic Engineering, 3(2-1), 21-24. https://doi.org/10.11648/j.jeee.s.2015030201.15

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ACS Style

Mohammad Seifi; Aliakbar Kargaran Erdechi; Ahmad Hajipour. Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks. J. Electr. Electron. Eng. 2014, 3(2-1), 21-24. doi: 10.11648/j.jeee.s.2015030201.15

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AMA Style

Mohammad Seifi, Aliakbar Kargaran Erdechi, Ahmad Hajipour. Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks. J Electr Electron Eng. 2014;3(2-1):21-24. doi: 10.11648/j.jeee.s.2015030201.15

Copy | Download

@article{10.11648/j.jeee.s.2015030201.15,
  author = {Mohammad Seifi and Aliakbar Kargaran Erdechi and Ahmad Hajipour},
  title = {Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {3},
  number = {2-1},
  pages = {21-24},
  doi = {10.11648/j.jeee.s.2015030201.15},
  url = {https://doi.org/10.11648/j.jeee.s.2015030201.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.s.2015030201.15},
  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.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Adaptive Noise Cancellation for Eliminating Artifacts of Life Signals Using Fuzzy and Neural Networks
AU  - Mohammad Seifi
AU  - Aliakbar Kargaran Erdechi
AU  - Ahmad Hajipour
Y1  - 2014/11/24
PY  - 2014
N1  - https://doi.org/10.11648/j.jeee.s.2015030201.15
DO  - 10.11648/j.jeee.s.2015030201.15
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 21
EP  - 24
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.s.2015030201.15
AB  - 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.
VL  - 3
IS  - 2-1
ER  -

Copy | Download