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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-fbc5a641-b00f-45fc-a850-b1750dffe94d

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

Artifacts removal from EEG signal: FLM optimization-based learning algorithm for neural network-enhanced adaptive filtering

Autorzy Quazi, M. H.  Kahalekar, S. G. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Electroencephalogram (EEG) denotes a neurophysiologic measurement, which observes the electrical activity of the brain through making a record of the EEG signal from the electrodes positioned on the scalp. The EEG signal gets mixed with other biological signals, called artifacts. Few artifacts include electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). Removal of artifacts from the EEG signal poses a great challenge in the medical field. Hence, the FLM (Firefly + Levenberg Marquardt) optimization-based learning algorithm for neural network-enhanced adaptive filtering model is introduced to eliminate the artifacts from the EEG. Initially, the EEG signal was provided to the adaptive filter for yielding the optimal weights using the renowned optimization algorithms, called firefly algorithm and LM. These two algorithms are effectively hybridized and applied to the neural network to find the optimal weights for adaptive filtering. Then, the designed filtering process renders an improved system for artifacts removal from the EEG signal. Finally, the performance of the proposed model and the existing models regarding SNR, computation time, MSE and RMSE are analyzed. The results conclude that the proposed method achieves a high SNR of 42.042 dB.
Słowa kluczowe
PL elektroencefalogram   algorytm Levenberga Marquardta   algorytm świetlika   stosunek sygnału do szumu  
EN electroencephalogram   Levenberg Marquardt algorithm   firefly algorithm   artifacts signal   signal to noise ratio  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2017
Tom Vol. 37, no. 3
Strony 401--411
Opis fizyczny Bibliogr. 26 poz., rys., tab., wykr.
Twórcy
autor Quazi, M. H.
autor Kahalekar, S. G.
  • Department of Instrumentation Engineering, Nanded, India
Bibliografia
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[3] Nguyen H-AT, Musson J, Li F, Wang W, Zhang G, Xu R, et al. EOG artifact removal using a wavelet neural network. Neurocomputing 2012;97:374–89.
[4] Turnip A, Junaidi E. Removal artifacts from EEG signal using independent component analysis and principal component analysis. Proceedings of International Conference on Technology, Informatics, Management, Engineering & Environment; 2014. p. 19–21.
[5] Vazquez RR, Perez HV, Ranta R, Louis VD, Maquin D, Maillard L. Blind source separation, wavelet de-noising and discriminant analysis for EEG artifacts and noise cancelling. Biomed Signal Process Control 2012;7:389–400.
[6] Geetha G, Geethalakshmi SN. Artifact removal from EEG using spatially constrained independent component analysis and wavelet de-noising with Otsu's thresholding technique. Procedia Eng 2012;30:1064–71.
[7] FemilinSheniha S, SujaPriyadharsini S, Edward Rajan S. Removal of artifact from EEG signal using differential evolution algorithm. Proceedings of International Conference on Communication and Signal Processing; 2013. p. 3–5.
[8] Gorecka J, Walerjan P. Artifacts extraction from EEG data using the infomax approach. Biocybern Biomed Eng 2011;31 (4):59–74.
[9] Correa G. Artifact removal from EEG signals using adaptive filters in cascade, vol. 90. IOP Publishing Ltd.; 2007. p. 1–10.
[10] Croft RJ, Barry RJ. Removal of ocular artifact from the EEG: a review. Neurophysiol Clin/Clin Neurophysiol 2000;30 (1):5–19.
[11] Li Y. Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach. Physiol Meas 2006;27:425–36.
[12] Senthilkumar P. Removal of ocular artifacts in the EEG through wavelet transform without using an EOG reference channel. Int J Open Probl Comput Sci 2008;1 (3):188–200.
[13] Gaidhane VH, Singh V, Hote YV, Kumar M. New approaches for image compression using neural network. Intell Learn Syst Appl 2011;3:220–9.
[14] Krishnaveni V. Automatic identification and Removal of ocular artifacts from EEG using wavelet transform. Meas Sci Rev 2006;6(4):45–57.
[15] Suja Priyadharsini S, Edward Rajan S. An efficient soft- computing technique for extraction of EEG signal from tainted EEG signal. Appl Soft Comput 2012;12:1131–7.
[16] Liu T, Yao D. Removal of the ocular artifacts from EEG data using a cascaded spatio-temporal processing. Comput Methods Prog Biomed 2006;83:95–103.
[17] Correa AG, Laciar E, Patiño HD, Valentinuzzi ME. Artifact removal from EEG signals using adaptive filters in cascade. J Phys Conf Ser 2007;90(1):1–10.
[18] Hsiao-Lung C, Yu-Tai T, Ling-Fu M, Tony W. The removal of ocular artifacts from EEG signals using adaptive filters based on ocular source components. Ann Biomed Eng 2010;38(11).
[19] Akhtar MT, Mitsuhasini W, James CJ. Employing spatially constrained ICA and wavelet de-noising, for automatic removal of artifacts from multichannel EEG data. Signal Process 2012;92:401–16.
[20] Jafarifarmand A, Badamchizadeh MA. Artifacts removal in EEG signal using a new neural network enhanced adaptive filter. Neurocomputing 2013;103:222–31.
[21] Zou Y, Nathan V, Jafari R. Automatic identification of artifact-related independent components for artifact removal in EEG recordings. Biomed Health Inform 2014;20 (1):73–81.
[22] Kim M-K, Kim S-P. Artifact removal from EEG signals using the total variation method. Proceedings of International Conference on ASCC; 2015. p. 1–4.
[23] Turnip A. Automatic artifacts removal of EEG signals using robust principal component analysis. Proceedings of International Conference on Technology, Informatics, Management, Engineering & Environment; 2014. p. 19–21.
[24] Guruva Reddy A, Narava S. Artifact removal from EEG signals. Comput Appl 2013;77(13).
[25] Menezes Jr JMP, Barreto GA. Long-term time series prediction with the NARX network: an empirical evaluation. Neuro Comput 2008;71:3335–43.
[26] Subhani Shareef SK, Rasul Mohideen E, Ali L. Directed Firefly algorithm for multimodal problems. Proceedings of International Conference on Computational Intelligence and Computing Research; 2015.
Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-fbc5a641-b00f-45fc-a850-b1750dffe94d
Identyfikatory
DOI 10.1016/j.bbe.2017.04.003