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Journal of Artificial Intelligence and Metaheuristics
Volume 1 , Issue 1, PP: 08-19 , 2022 | Cite this article as | XML | Html |PDF

Title

Removing Powerline Interference from EEG Signal using Optimized FIR Filters

  Mohamed Saber 1 *

1  Electronics and Communications Engineering Dep., Faculty of Engineering, Delta University for Science and Technology, Gamasa City, Mansoura, Egypt
    (Mohamed.saber@deltauniv.edu.eg)


Doi   :   https://doi.org/10.54216/JAIM.010101

Received: January 05, 2022 Accepted: May 15, 2022

Abstract :

The Electroencephalography (EEG) is a signal representing the electrical activity of the brain and is used in the diagnosis of brain diseases. The EEG signal is weak and highly prone to noise from the powerline which generates a sinusoidal signal with the main frequency of 50/60 Hz. Therefore, three harmonics of powerline noise must be removed using notch filters for a perfect diagnosis which requires three series notch filters. This paper presents a new method to design a digital notch finite impulse response (FIR) filter using a modified particle swarm optimization technique. The proposed method provides a short length, maximum stopband attenuation, and small transition width compared to different algorithms which results in removing the noise in EEG signal efficiently.

Keywords :

EEG; power line interference ; Notch FIR filter.

References :

[1] William O. Tatum, (Demos Medical) (2021). Handbook of EEG Interpretation.

[2] Neville M. Jadeja, (Cambridge UNIVERSITY Press) (2021). How to Read an EEG.

[3] Michalis K. , (John Libbey Eurotext) (2021). The role of EEG in the diagnosis and classification

of the epilepsies and the epilepsy syndromes: A tool for clinical practice.

[4] M. M. Eid, F. Alassery, A. Ibrahim and M. Saber, Metaheuristic optimization algorithm for

signals classification of electroencephalography channels. Computers, Materials &

Continua,71(3), 4627–4641, 2022.

[5] Proakis, J.G. , & Manolakis, (Pearson Education) (2007). Digital signal Processing Principles,

Algorithms, and Applications.

[6] Li tan, J. Hiang, (Academic Press) (2013). Digital Signal processing fundamentals and

applications.

[7] N. Singh, A. Potnis. A review of different optimization algorithms for a linear phase FIR filters,

International Conference on recent Innovations in Signal Processing and embedded Systems

(RISE), 44-48, 2017.

[8] Wu, Chen, et al., Spare FIR Design Based on Simulated Annealing Algorithm, 15(1), 2015.

[9] K. Watcharasitthiwat, J. koseeyaporn, P. Wardkein, Designing Digital Filters Using Multiple

Tabu Search Algorithm , International Conference on Communications, Circuits and Systems,

171-175, 2006.

[10] A. Kumar, S. Ghosh, N. Londhe, Modified artificial bee colony optimization-based FIR filter design

with experimental validation using field-programmable gate array, IET Signal Processing, 10 (8), 955-

964, 2016.

[11] Adel Jalal Yousif, Ghazwan Jabbar Ahmed and Ali Subhi Abbood, Design of Linear Phase High Pass

FIR Filter using Weight Improved Particle Swarm Optimization, International Journal of Advanced

Computer Science and Applications, 9(9), 2018.

[12] N. Karaboga and B. Cetinkaya, “Design of digital FIR filters using differential evolution

algorithm,” Circuits, Systems, and Signal Processing, 25 (5), 649–660, 2006.

[13] G. Liu, Y. X. Li, and G. He, “Design of digital FIR filters using differential evolution algorithm based

on reserved gene,” in Proceedings of the IEEE Congress on Evolutionary Computation, 1–7, 2010.

[14] J. Kennedy and R. Eberhart, Particle swarm optimization, Proceedings of the IEEE International

Conference on Neural Networks, 4, 1942-1948, 1995.

[15] D. Karaboga, D.H. Horrocks, N. Karaboga, A. Kalinli, Designing digital FIR filters using Tabu search

algorithm, IEEE International Symposium on Circuits and Systems, 4, 2236-2239, 1997.

[16] G. Liu, Y.X. Li, and G.He, Design of Digital FIR Filters Using Differential Evolution Algorithm Based

on Reserved Gene, IEEE Congress on Evolutionary Computation,1-7, 2010.

[17] M. Najjarzadeh, A.Ayatollahi, FIR Digital Filters Design: Particle Swarm Optimization Utilizing LMS

and Minimax Strategies, International symp. on Signal Processing and Information Technology,

ISSPIT, 129-132, 2008.

[18] J.I. Ababneh, M. H. Bataineh, Linear phase FIR filter design using particle swarm optimization and

genetic algorithms, Digital Signal Processing, 18, 657–668, 2008.

[19] A. Sarangi, R.K. Mahapatra, S.P. Panigrahi, DEPSO and PSO-QI in digital filter design, Expert

Systems with Applications, 2011, vol. 38, No.9, pp.10966-10973.

[20] B. Luitel, G. K. Venayagamoorthy, Differential Evolution Particle Swarm Optimization for Digital

Filter Design, IEEE Congress on Evolutionary Computation, 3954- 3961, 2008.

[21] El-Sayed M. El-Kenawy, Seyedali Mirjalili, Fawaz Alassery, Yu-Dong Zhang, Marwa Metwally Eid,

Shady Y. El-Mashad, Bandar Abdullah Aloyaydi, Abdelhameed Ibrahim, and Abdelaziz A.

Abdelhamid.

Novel Meta-Heuristic Algorithm for Feature Selection, Unconstrained Functions and Engineering Prob

lems. IEEE Access, 10:40536–40555, 2022.

[22] Abdelaziz A. Abdelhamid, El-Sayed M. El-Kenawy, Bandar Alotaibi, Ghada M. Amer, Mahmoud Y.

Abdelkader, Abdelhameed Ibrahim, and Marwa Metwally Eid. Robust Speech Emotion Recognition

Using CNN+LSTM Based on Stochastic Fractal Search Optimization Algorithm. IEEE Access,

10:49265–49284, 2022.

[23] Doaa Sami Khafaga, Amel Ali Alhussan, El-Sayed M. El-kenawy, Ali E. Takieldeen, Tarek M. Hassan,

Ehab A. Hegazy, Elsayed Abdel Fattah Eid, Abdelhameed Ibrahim, and Abdelaziz A. Abdelhamid.

Meta-heuristics for Feature Selection and Classification in Diagnostic Breast-Cancer. Computers,

Materials & Continua, 73(1):749–765, 2022.

[24] Doaa Sami Khafaga, Amel Ali Alhussan, El-Sayed M. El-kenawy, Abdelhameed Ibrahim, Said H. Abd

Elkhalik, Shady Y. El-Mashad, and Abdelaziz A. Abdelhamid. Improved Prediction of Metamaterial

Antenna Bandwidth Using Adaptive Optimization of LSTM. Computers, Materials & Continua,

73(1):865–881, 2022.

[25] Nagwan Abdel Samee, El-Sayed M. El-Kenawy, Ghada Atteia, Mona M. Jamjoom, Abdelhameed

Ibrahim, Abdelaziz A. Abdelhamid, Noha E. El-Attar, Tarek Gaber, Adam Slowik, and Mahmoud Y.

Shams. Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest XRay

Images. Computers, Materials & Continua, 73(2):4193–4210, 2022.

[26] Hussah Nasser AlEisa, El-Sayed M. El-kenawy, Amel Ali Alhussan, Mohamed Saber, Abdelaziz A.

Abdelhamid, and Doaa Sami Khafaga. Transfer Learning for Chest X-rays Diagnosis Using Dipper

Throated Algorithm. Computers, Materials & Continua, 73(2):2371–2387, 2022.

[27] Doaa Sami Khafaga, Amel Ali Alhussan, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Marwa

Metwally Eid, and Abdelaziz A. Abdelhamid. Solving optimization problems of metamaterial and

doublet-shape antennas using advanced meta-heuristics algorithms. IEEE Access, 10:74449–74471,

2022.

[28] Hala Hassan, Ali Ibrahim El-Desouky, Abdelhameed Ibrahim, El-Sayed M. El-Kenawy, and Reham

Arnous. Enhanced QoS-Based Model for Trust Assessment in Cloud Computing Environment. IEEE

Access, 8:43752–43763, 2020.

[29] Marwa M. Eid, El-Sayed M. El-kenawy, and Abdelhameed Ibrahim. A binary Sine Cosine-Modified

Whale Optimization Algorithm for Feature Selection. In 2021 National Computing Colleges

Conference (NCCC), pages 1–6, Taif, Saudi Arabia, March 2021. IEEE.

[30] El-Sayed M. El-Kenawy, Seyedali Mirjalili, Sherif S. M. Ghoneim, Marwa Metwally Eid, M. El-Said,

Zeeshan Shafi Khan, and Abdelhameed Ibrahim. Advanced Ensemble Model for Solar Radiation

Forecasting Using Sine Cosine Algorithm and Newton’s Laws. IEEE Access, 9:115750–115765, 2021.

[31] Abdullah Ali Salamai, El-Sayed M. El-kenawy, and Ibrahim Abdelhameed. Dynamic Voting Classifier

for Risk Identification in Supply Chain 4.0. Computers, Materials & Continua, 69(3):3749–3766, 2021.

[32] Abdelhameed Ibrahim, Seyedali Mirjalili, M. El-Said, Sherif S. M. Ghoneim, Mosleh M. Al-Harthi,

Tarek F. Ibrahim, and El-Sayed M. El-Kenawy. Wind Speed Ensemble Forecasting Based on Deep

Learning Using Adaptive Dynamic Optimization Algorithm. IEEE Access, 9:125787–125804, 2021.

[33] El-Sayed M. El-kenawy, Hattan F. Abutarboush, Ali Wagdy Mohamed, and Abdelhameed Ibrahim.

Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna.

Computers, Materials & Continua, 69(3):2983–2995, 2021.


Cite this Article as :
Style #
MLA Mohamed Saber. "Removing Powerline Interference from EEG Signal using Optimized FIR Filters." Journal of Artificial Intelligence and Metaheuristics, Vol. 1, No. 1, 2022 ,PP. 08-19 (Doi   :  https://doi.org/10.54216/JAIM.010101)
APA Mohamed Saber. (2022). Removing Powerline Interference from EEG Signal using Optimized FIR Filters. Journal of Journal of Artificial Intelligence and Metaheuristics, 1 ( 1 ), 08-19 (Doi   :  https://doi.org/10.54216/JAIM.010101)
Chicago Mohamed Saber. "Removing Powerline Interference from EEG Signal using Optimized FIR Filters." Journal of Journal of Artificial Intelligence and Metaheuristics, 1 no. 1 (2022): 08-19 (Doi   :  https://doi.org/10.54216/JAIM.010101)
Harvard Mohamed Saber. (2022). Removing Powerline Interference from EEG Signal using Optimized FIR Filters. Journal of Journal of Artificial Intelligence and Metaheuristics, 1 ( 1 ), 08-19 (Doi   :  https://doi.org/10.54216/JAIM.010101)
Vancouver Mohamed Saber. Removing Powerline Interference from EEG Signal using Optimized FIR Filters. Journal of Journal of Artificial Intelligence and Metaheuristics, (2022); 1 ( 1 ): 08-19 (Doi   :  https://doi.org/10.54216/JAIM.010101)
IEEE Mohamed Saber, Removing Powerline Interference from EEG Signal using Optimized FIR Filters, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 1 , No. 1 , (2022) : 08-19 (Doi   :  https://doi.org/10.54216/JAIM.010101)