EEG Eye Blink Artifacts Removal with Wavelet Denoising and Bandpass Filtering
DOI:
https://doi.org/10.21123/bsj.2024.8947Keywords:
Analyzing of Signal, Blinking of Eye, Electroencephalogram (EEG), Processing of Bandpass Filtering (BPF), Wavelet Transformation (WT).Abstract
Many data sources can be analyzed using Wavelet Transforms (WT), a mathematical technique frequently used for extracting information from them. Although WT was effective at Blind Source Separation (BSS), it had some limitations, such as signal loss. The problem has been addressed with the introduction of a joint algorithm that combines WT with Frequency Domain Filtering (BPF). Wavelet Denoising Technique (WDT) and Band-Pass Filtering (BPF) are employed in this research to propose an innovative algorithm for combining advanced wavelet transform methods. Combining these two techniques helps reduce eye flutter in electroencephalograms (EEGs).FP1 signals produced by eye movement are filtered out by this novel algorithm. EEG signals should be captured dependably. Combined WTs perform better than traditional WTs, according to evidence. Based on Signal-to-Noise-Ration (SNR) and Power Spectral Density (PSD) measurements, the removal process has been demonstrated to be more efficient than a standard WT.
Received 17/04/2023
Revised 12/12/2023
Accepted 14/12/2023
Published Online First 20/04/2024
References
Heyat MB, Akhtar F, Khan A, Noor A, Benjdira B, Qamar Y, et al. A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals. Appl Sci. 2020; 10 (21): 7410. https://doi.org/10.3390/app10217410
Siddiqui MM, Srivastava G, Saeed SH. Diagnosis of insomnia sleep disorder using short time-frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC. Sleep Sci. 2016; 9(3): 186-91. https://doi.org/10.1016/j.slsci.2016.07.002
Höller Y, Helmstaedter C, Lehnertz K. Quantitative pharmaco-electroencephalography in antiepileptic drug research. CNS drugs. 2018; 32(9): 839-48. https://doi.org/10.1007/s40263-018-0557-x
Kim CS, Sun J, Liu D, Wang Q, Paek SG. Removal of ocular artifacts using ICA and adaptive filter for motor imagery-based BCI. IEEE/CAA J. Autom Sin. . 2017. https://doi.org/ 10.1109/JAS.2017.7510370
Gómez A, Tsanas A, Gómez P, Palacios-Alonso D, Rodellar V, Álvarez A. Acoustic to kinematic projection in Parkinson’s Disease Dysarthria. Biomed Signal Process Control. 2021; 66: 102422. https://doi.org/10.1016/j.bspc.2021.102422
Noureddin B, Lawrence PD, Birch GE. Online removal of eye movement and blink EEG artifacts using a high-speed eye tracker. IEEE Trans Biomed Eng. 2011; 59(8): 2103-10. https://doi.org/10.1109/TBME.2011.2108295
Ghandeharion H, Erfanian A. A fully automatic ocular artifact suppression from EEG data using higher-order statistics: Improved performance by wavelet analysis. Med Eng Phys. 2010; 32(7): 720-9. https://doi.org/10.1016/j.medengphy.2010.04.010
Rasheed T, Lee YK. Constrained blind source separation of human brain signals (Doctoral dissertation, PhD Thesis, Department of Computer Engineering, Kyung Hee University, Seoul, Korea). https://doi.org/10.1007/978-3-540-92841-6_136.
Ahmed A, Zhu ZC, Yan C. A novel evolutionary approach for blind source separation based on stone's method. IEEE Int Conf Signal Process. 2012; 1: 324-328. IEEE. https://doi.org/10.1109/ICoSP.2012.6491666.
Borah BB, Hazarika U, Baruah SM, Roy S, Jamir A. A BCI framework for smart home automation using EEG signal. 2023 (Preprint): 1-9. https://doi.org/10.3233/IDT-220224
Jurczak M, Kołodziej M, Majkowski A. Implementation of a convolutional neural network for eye blink artifacts removal from the electroencephalography signal. Front Neurosci. 2022; 16: 782367. https://doi.org/10.3389/fnins.2022.782367
Giudice ML, Varone G, Ieracitano C, Mammone N, Bruna AR, Tomaselli V, Morabito FC. 1D Convolutional Neural Network approach to classify voluntary eye blinks in EEG signals for BCI applications. Proc Int Jt Conf Neural Netw. 2020; 1-7. IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207195
Mayeli A, Al-Zoubi O, Henry K, Wong CK, White EJ, Luo Q, et al. Tulsa 1000 Investigators. APPEAR recorded during fMRI. J Neural Eng. 2021; 18(4): 0460b4. https://doi.org/10.1088/1741-2552/ac1037
Mathe M, Padmaja M, Krishna BT. Intelligent approach for artifacts removal from EEG signal using heuristic-based convolutional neural network. Biomed Signal Process Control. 2021; 70: 102935. https://doi.org/10.1016/j.bspc.2021.102935
Krishnaveni V, Jayaraman S, Aravind S, Hariharasudhan V, Ramadoss K. Automatic identification and removal of ocular artifacts from EEG using wavelet transform. Meas Sci Rev. 2006; 6(4): 45-57.
Hamdi MM, Mustafa AS, Mahd HF, Abood MS, Kumar C, Al-shareeda MA. Performance Analysis of QoS in MANET based on IEEE 802.11 b. Int Conf Innov Manag. 2020 (pp. 1-5). IEEE. https://doi.org/10.1109/INOCON50539.2020.9298362
Al-Shareeda MA, Manickam S. MANETs: Analysis and evaluation. Sym. 2022; 14(8):1543. https://doi.org/10.3390/sym14081543
Al-Mekhlafi ZG, Al-Shareeda MA, Manickam S, Mohammed BA, Qtaish A. Lattice-based lightweight quantum resistant scheme in 5G-enabled vehicular networks. Math. 2023; 11(2): 399. https://doi.org/10.3390/math11020399
Mohammed BA, Al-Shareeda MA, Manickam S, Al-Mekhlafi ZG, Alreshidi A, Alazmi M, et al. FC-PA: fog computing-based pseudonym authentication scheme in 5G-enabled vehicular networks. IEEE Access. 2023; 11: 18571-81. https://doi.org/ 10.1109/ACCESS.2023.3247222
Al-Mekhlafi ZG, Al-Shareeda MA, Manickam S, Mohammed BA, Alreshidi A, Alazmi M, et al. Chebyshev polynomial-based fog computing scheme supporting pseudonym revocation for 5G-enabled vehicular networks. Electronics. 2023; 12(4): 872. https://doi.org/10.3390/electronics12040872
Al-Shareeda MA, Manickam S. COVID-19 vehicle based on an efficient mutual authentication scheme for 5G-enabled vehicular fog computing. Int J Environ Res Public Health. 2022; 19(23): 15618. https://doi.org/10.3390/ijerph192315618
Ashraf S, Alfandi O, Ahmad A, Khattak AM, Hayat B, Kim KH, et al. Bodacious-instance coverage mechanism for wireless sensor network. Wirel Commun Mob Comput. 2020; 2020: 1-1. https://doi.org/10.1155/2020/8833767
Ahmad A, Ullah A, Feng C, Khan M, Ashraf S, Adnan M, et al. Towards an improved energy efficient and end-to-end secure protocol for iot healthcare applications. Secur. Commun. Netw. 2020; 2020: 1-0. https://doi.org/10.1155/2020/8867792
Ashraf S, Ahmed T, Saleem S. NRSM: Node redeployment shrewd mechanism for wireless sensor network. Iran J Comput Sci. 2021; 4(3): 171-83. https://doi.org/10.1007/s42044-020-00075-x
Islam MK, Rastegarnia A, Yang Z. Methods for artifact detection and removal from scalp EEG: A review. Neurophysiol Clin. 2016; 46(4-5): 287-305. https://doi.org/10.1016/j.neucli.2016.07.002
Zeng H, Song A, Yan R, Qin H. EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition. Sens. 2013; 13(11): 14839-59. https://doi.org/10.3390/s131114839
Valderrama JT, De La Torre A, Van Dun B. An automatic algorithm for blink-artifact suppression based on iterative template matching: Application to single channel recording of cortical auditory evoked potentials. J Neural Eng. 2018 15(1): 016008. https://doi.org/10.1088/1741-2552/aa8d95
Kher R, Gandhi R. Adaptive filtering based artifact removal from electroencephalogram (EEG) signals. International Conference on Communication and Signal Processing (ICCSP). 2016; 0561-0564. IEEE. https://doi.org/10.1109/ICCSP.2016.7754202
Abdullah AK, Zhang CZ, Lian SY. Separation of EOG Artifact and Power Line Noise-50Hz from EEG by Efficient Stone's BSS Algorithm. Appl Mech Mater. 2014; 543: 2687-91. https://doi.org/10.4028/www.scientific.net/AMM.543-547.2687
Singh B, Wagatsuma H. A removal of eye movement and blink artifacts from EEG data using morphological component analysis. Comput Math Methods Med. 2017; 2017. https://doi.org/10.1155/2017/1861645
Suja Priyadharsini S, Edward Rajan S, Femilin Sheniha S. A novel approach for the elimination of artefacts from EEG signals employing an improved Artificial Immune System algorithm. J Exp Theor Artif Intell. 2016; 28(1-2): 239-59. https://doi.org/10.1080/0952813X.2015.1020571
Dhiman R, Saini JS, Priyanka AM. Artifact removal from EEG recordings–an overview. Proc. NCCI 2010: 1-6.
Mowla MR, Ng SC, Zilany MS, Paramesran R. Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising. Biomed Signal Process Control. 2015; 22: 111-8. https://doi.org/10.1016/j.bspc.2015.06.009
Saleh BJ, Saedi AYF, al-Aqbi ATQ, Salman L abdalhasan. Optimum Median Filter Based on Crow Optimization Algorithm. Baghdad Sci J. 2021; 18(3): 0614. http://dx.doi.org/10.21123/bsj.2021.18.3.0614
Abbas HK, Al-Saleh AH, Mohamad HJ, Al-Zuky AA. New algorithms to enhanced fused images from auto-focus images. Baghdad Sci J. 2021; 10(18): 1. http://dx.doi.org/10.21123/bsj.2020.18.1.0124
Mohammadi Z, Frounchi J, Amiri M. Wavelet-based emotion recognition system using EEG Neural Comput Appl. 2017; 28(8): 1985-90. https://doi.org/10.1007/s00521-015-2149-8
Javidi S, Mandic D, Cheong Took C, Cichocki A. Kurtosis based blind source extraction of complex noncircular signals with application in EEG artifact removal in real-time. Front Neurosci. 2011; 5: 105. https://doi.org/10.3389/fnins.2011.00105
Chella F, Pizzella V, Zappasodi F, Marzetti L. Impact of the reference choice on scalp EEG connectivity estimation. J Neural Eng. 2016 X; 13(3): 036016. https://doi.org/10.1088/1741-2560/13/3/036016
Hu S, Karahan E, Valdes-Sosa PA. Restate the reference for EEG microstate analysis. arXiv preprint arXiv:1802.02701. 2018 https://doi.org/10.48550/arXiv.1802.02701
Sebek J, Bortel R, Sovka P. Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records. PloS one. 2018; 13(8): e0201900. https://doi.org/10.1371/journal.pone.0201900
Egan MK, Larsen R, Wirsich J, Sutton BP, Sadaghiani S. Safety and data quality of EEG recorded simultaneously with multi-band fMRI. PloS one. 2021; 16(7): e0238485. https://doi.org/10.1371/journal.pone.0238485
Hossain MB, Bashar SK, Lazaro J, Reljin N, Noh Y, Chon KH. A robust ECG denoising technique using variable frequency complex demodulation. Comput Methods Programs Biomed. 2020; 105856. https://doi.org/10.1016/j.cmpb.2020.105856
Wang Y, Fu Y, He Z. Fetal electrocardiogram extraction based on fast ICA and wavelet denoising. 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). 2018: 466-469. IEEE. https://doi.org/10.1109/IMCEC.2018.8469501
Avendano LE, Padilla JI, Delgado-Trejos E, Cuesta-Frau D. Detection of ECG Fiducial Points Using Recursive Estimation and Kalman Filtering. Comput Cardiol. 2020:1-4. IEEE. https://doi.org/10.22489/CinC.2020.284
Downloads
Issue
Section
License
Copyright (c) 2024 Ebtesam N. AlShemmary , Bushra K. Hilal , Azmi Sh. Abdulbaqi , Mohammed A. Ahmed , Zhentai Lu
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
