Unsupervised Eye Blink Artifact Identification in Electroencephalogram

Egambaram, A. and Badruddin, N. and Asirvadam, V.S. and Fauvet, E. and Stolz, C. and Begum, T. (2019) Unsupervised Eye Blink Artifact Identification in Electroencephalogram. [["eprint_typename_conference\_item" not defined]]

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....


The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error rate in detecting events of EB artifacts in EEG signals. Analysis has revealed that the proposed approach achieved an average of 96.6 accuracy compared to a conventional method of identifying EB artifacts with a fixed constant threshold. © 2018 IEEE.

Item Type:["eprint_typename_conference\_item" not defined]
Impact Factor:cited By 3
Uncontrolled Keywords:Signal processing, Amplitude displacements; Automated Threshold; Conventional methods; EB Artifacts; EEG signal processing; Electroencephalogram signals; Eye-blink artifacts; Threshold determination, Electroencephalography
ID Code:23633
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 08:09
Last Modified:19 Aug 2021 08:09

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