Evaluation of Filtering Techniques applied to simulated Electroencephalogram signals for Visual Evoked Potential Detection
PDF
XML

Keywords

VEP
filter
latency
wavelet
electroencephalographic signals
simulation
eye diseases
simulated signals
latency parameters
extraction methods
noise PEV
filtrado
latencia
wavelet
señales electroencefalográficas
simulación
enfermedades oculares
señales simuladas
parámetros de latencia
métodos de extracción
ruido

How to Cite

Montalvo-Aguilar, J. A., Bazán, I., & Ramírez-García, A. (2020). Evaluation of Filtering Techniques applied to simulated Electroencephalogram signals for Visual Evoked Potential Detection. Nova Scientia, 12(25). https://doi.org/10.21640/ns.v12i25.2374

Abstract

Introduction: In this paper, it is presented an assessment of different windowing techniques and denoising methods applied to detect the Visual Evoked Potential (VEP) contained into the electroencephalographic (EEG) signals. The objective is to analyze advantages and disadvantages of each technique performance to identify correctly the VEP waveform and to quantify the latency which is a parameter commonly used as an indicator of ocular diseases.                       

Method: Assessment of techniques was performed considering rigorous controlled conditions on the signal set, to assure that obtained results were linked just to the technique performances and avoid any undesirable effect due to the natural interference of acquisition of signals. For this reason, a simulated signals set was created based on a typical VEP waveform commonly registered around 100ms after an external stimulus was applied in the routine clinical study. Additionally, two evaluation stages were considered: fixed latency parameters stage and random latency parameters stage.

Results: The best results without filter was with rectangular window and the use of elliptic filter can help to extract the VEP with a rectangular window too. For wavelet denoising the best result is Biorthogonal 2.6 wavelet with a Hamming window.                       

Discussion or Conclusion: Five parameters were proposed to assessment the VEP extraction performed: signal noise ratio (SNR) mean square error (MSE), average latency (AL), latency standard deviation (LSD) and latency correlation variance (LCV) as representative factors to be considered on evaluations of VEP extraction methods. SNR and MSE were focused to assess the level of noise that remain in signal after windowing & denoising method was applied. In the other hand AL, LSD and LCV were oriented to evaluate the impact of the method on the VEP latency estimation.

https://doi.org/10.21640/ns.v12i25.2374
PDF
XML

References

Dulce Lourdes Loza pacheco, 2013. La disminución de la agudeza visual. Su dimensión, repercusión social y económica de México. CINVESTAV IPN.

Robert N. Weinreb, 2007. Glaucoma neuroprotection: What is it? Why is it needed? MD, CAN J OPHTHALMOL—VOL. 42, NO. 3.

Masaki Nakanishi, Yu-TeWang; Tzyy-Ping Jung, 2017. Detecting Glaucoma with a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss. JAMA Ophthalmology Volume 135, Number 6.

Keith W Mitchell, Christopher M Wood, John W Howe, William H Church, George T H Smith, and Stephen R Spencer, 1989. The visual evoked potential in acute primary angle closure glaucoma From the Regional Medical Physics Department and University Department of Ophthalmology, Royal Victoria Infirmary, Newcastle upon Tyne.

Vernon L. Towle, 1983. The Visual Evoked Potential in Glaucoma and Ocular Hypertension: Effects of Check Size, Field Size, and Stimulation Rate. Invest. Ophthalmol. Vis.Sci; 24(2):175-183.

Kothari Ruchi, 2012. The Potential Use of Pattern Reversal Visual Evoked Potential for Detecting and Monitoring Open Angle Glaucoma. Current Neurobiology; 3 (1): 39-45

Donnell Creel, 1995. The Organization of the Retina and Visual System. Cap Electrophysiology: The Study of Light Evoked Filed Potentials.

José García, 2014. Análisis de potenciales evocados multifocales aplicados a neuropatías, Universidad de Alcalá, España.

Macías Castillo, M. J. 2014. Procesado de la señal mfVEP, Universidad de Alcalá, España.

François-Benoît Vialatte, Monique Maurice, Justin Dauwels, 2010.Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives. Progress in NeuroBiology Volume 90, issue 4.

Dennis J. McFarland*, Laurie A. Miner*, Theresa M. Vaughan*, and Jonathan R. Wolpaw*, 2000. “Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements” Brain Topography, Volume 12. Number 3, pp. 177-186.

D. Liu, B. Sun, C. Chang, J. Yang, J. Wang, and N. Hu, 2017. Extraction of Visual Evoked Potential using Improved Wiener Filter. School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu China.

Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, 2016. Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs.

Robi Polikar, 2006. The engineer’s ultimate guide to wavelet analysis.

Barde MP, Barde PJ., 2012. What to use to express the variability of data: Standard deviation or standard error of mean? Perspect Clin Res.3(3):113‐116.

Hervé Abdi, 2010. Encyclopedia of Research Design. Coefficient of Variation.

Donoho, D. and Johnstone, 1994. Ideal spatial adaptation by wavelet shrinkage, Biometrika,81,3, 425–455.

R. Mahajan, B. I. Morshed, 2015. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA. IEEE Journal of Biomedical and Health Informatics, vol.19, no.1, pp.158-165,

R. Wang, 2015. Extraction of auditory and visual evoked potential based on rough sets theory and Informax ICA Algorithm Research. Master Thesis, Guangdong University of Technology.

S. Lemm, G. Curio, Y. Hlushchuk, 2006. Enhancing the signal to noise ratio of ICA-based extracted ERPs. IEEE Transactions on Biomedical Engineering, vol.53, no.4, pp.601–607.

R. Alain, G. Vincent, 2008. BCI competition III: Ensemble of SVMs for BCI P300 speller. IEEE Transactions on Biomedical Engineering, vol.55, no.3, pp.1147.

Hoffmann M.B., Straube S., Bach M., 2003. Pattern-onset stimulation boost central multifocal responses, Journal of Vision 3, 432-439.

Donnell Creel, 2015. The Organization of the Retina and Visual System, Visually Evoked Potentials. Moran Eye Center, University of Utah.

Chiappa,1997. Evoked potentials in clinical medicine, Philadelphia: Lippincott-Raven.

Syaimaa’ Solehah, 2017. Evaluation of simulated VEP signals on basis of Higuchi and Katz’s algorithm. 20147 IEEE International Conference on Signal and Image Processing Application (IEEE ICSIPA 2017). Pp 310-314.

Huang G., Meng J., Zhang D., Zhu X. (2011) Window Function for EEG Power Density Estimation and Its Application in SSVEP Based BCIs. In: Jeschke S., Liu H., Schilberg D. (eds) Intelligent Robotics and Applications. ICIRA 2011. Lecture Notes in Computer Science, vol 7102. Springer, Berlin, Heidelberg.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2020 Nova Scientia