Computational Method for Continuous Monitoring of Ocular Artifacts in Electroencephalography Data

Authors

DOI:

https://doi.org/10.22456/2175-2745.150325

Keywords:

EEG, EOG, ocular artifacts, real-time processing, assistive technology

Abstract

This work presents a computational method for detecting ocular artifacts in EEG/EOG signals, focusing on Brain-Computer Interfaces and Assistive Technologies. The system uses an Arduino Nano and AD8232 module for real-time acquisition and processing, implementing an algorithm based on voltage thresholds and signal derivative analysis. In a 10-minute trial, 248 blinks were detected, of which 210 were real and 38 false, with only 6 missed. The method achieved an accuracy of 84.7% and a sensitivity of 97.2%, demonstrating effectiveness in identifying real blinks and potential for EEG pre-processing and Assistive Control applications.

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Author Biographies

Yuri Silveira Pereira, Universidade Federal de Santa Maria

Federal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Santana do Livramento Campus, Santana do Livramento, Rio Grande do Sul, Brazil; Federal University of Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil.

Daniel Dargelio Ferrão Luft, Universidade Federal de Santa Maria

Federal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Santana do Livramento Campus, Santana do Livramento, Rio Grande do Sul, Brazil; Federal University of Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil.

Matheus Gularte Tavares, Instituto Federal de Educação, Ciência e Tecnologia Sul-rio-grandense

Federal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Santana do Livramento Campus, Santana do Livramento, Rio Grande do Sul, Brazil.

Tadeu Vargas, Instituto Federal de Educação, Ciência e Tecnologia Sul-rio-grandense

Federal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Santana do Livramento Campus, Santana do Livramento, Rio Grande do Sul, Brazil.

Samuel dos Santos Cardoso, Instituto Federal de Educação, Ciência e Tecnologia Sul-rio-grandense

Federal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Santana do Livramento Campus, Santana do Livramento, Rio Grande do Sul, Brazil.

References

[1] SAWANGJAI, P. et al. EEGANet: removal of ocular artifacts from the EEG signal using generative adversarial networks. Journal of Neural Engineering, v. 26, n. 10, p. 4913–4924, 2022.

[2] PREM, S. et al. BCI integrated wheelchair controlled via eye blinks and brain waves. In: INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SOCIETAL APPLICATIONS, 2020. [S.l.]: Springer International Publishing, 2021. p. 321–331.

[3] AZIZ, F. et al. HMM based automated wheelchair navigation using EOG traces in EEG. Journal of Neural Engineering, v. 11, n. 5, p. 056018, 2014.

[4] PANERU, B.; THAPA, B.; POUDYAL, K. N. EEG-based AI-BCI wheelchair advancement: a brain-computer interfacing wheelchair system using deep learning approach. 2024. Preprint (arXiv). Available at: https://arxiv.org/abs/2410.09763. Accessed on: May 29, 2025.

[5] FAVRETTO, M. A. et al. High density surface EMG system based on ADS1298-front end. IEEE Latin America Transactions, v. 16, n. 6, p. 1616–1622, 2018.

[6] JÚNIOR, J. J. A. M. et al. AD8232 to biopotentials sensors: Open source project and benchmark. Electronics, v. 12, n. 4, 2023.

[7] JASPER, H. H. The ten-twenty electrode system of the international federation. Electroencephalography and Clinical Neurophysiology, v. 10, p. 371–375, 1958.

[8] SANEI, S.; CHAMBERS, J. A. EEG signal processing. [S.l.]: Wiley-Interscience, 2007.

[9] BITBRAIN. EEG amplifier: what it is, how it works and how to choose the right one. 2021. Bitbrain Blog. Available at: https://www.bitbrain.com/blog/eeg-amplifier. Accessed on: May 29, 2025.

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Published

2026-08-10

How to Cite

Silveira Pereira, Y., Dargelio Ferrão Luft, D., Gularte Tavares, M., Vargas, T., & dos Santos Cardoso, S. (2026). Computational Method for Continuous Monitoring of Ocular Artifacts in Electroencephalography Data. Revista De Informática Teórica E Aplicada, 33(4), 44–52. https://doi.org/10.22456/2175-2745.150325

Issue

Section

Regular Papers
Received 2025-09-19
Accepted 2026-06-27
Published 2026-08-10

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