Abstract
Blink detection and removal is a very challenging task that needs to be solved in order to perform several EEG signal analyses, especially when an online analysis is required. This study is focused on the comparison of three different techniques for blink detection and three more for blink removal; one of the techniques has been enhanced in this study by determining the dynamic threshold for each participant instead of having a common value. The experimentation first compares the blink detection and then, the best method is used for the blink removal comparison. A real data set has been gathered with healthy participants and a controlled protocol, so the eye blinks can be easily labelled. Results show that some methods performed surprisingly poor with the real data set. In terms of blink detection, the participant-tuned dynamic threshold was found better than the others in terms of Accuracy and Specificity, while comparable with the Correlation-based method. In terms of blink removal, the combined CCA+EEMD algorithm removes better the blink artifacts, but the DWT one is considerably faster than the others.
This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R, PID2020-112726RB-I00 and the State Research Agency (AEI, Spain) under grant agreement No RED2018-102312-T (IA-Biomed). Additionally, by the Council of Gijón through the University Institute of Industrial Technology of Asturias grant SV-21-GIJON-1-19.
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Moncada, F., González, V.M., García, B., Álvarez, V., Villar, J.R. (2021). A Comparison of Blink Removal Techniques in EEG Signals. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_30
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