IMR Press / JIN / Volume 18 / Issue 3 / DOI: 10.31083/j.jin.2019.03.164
Open Access Original Research
Automatic eye blink artifact removal for EEG based on a sparse coding technique for assessing major mental disorders
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1 Department of Engineering, National University of Modern Languages, Islamabad 44000, Pakistan
2 Centre for Intelligent Signal & Imaging Research (CISIR), Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia
*Correspondence: (Abdul Qayyum)
J. Integr. Neurosci. 2019, 18(3), 217–229;
Submitted: 16 May 2019 | Accepted: 23 July 2019 | Published: 30 September 2019
Copyright: © 2019 Zafar et al. Published by IMR press.
This is an open access article under the CC BY-NC 4.0 license

In the electroencephalogram recorded data are often confounded with artifacts, especially in the case of eye blinks. Different methods for artifact detection and removal are discussed in the literature, including automatic detection and removal. Here, an automatic method of eye blink detection and correction is proposed where sparse coding is used for an electroencephalogram dataset. In this method, a hybrid dictionary based on a ridgelet transformation is used to capture prominent features by analyzing independent components extracted from a different number of electroencephalogram channels. In this study, the proposed method has been tested and validated with five different datasets for artifact detection and correction. Results show that the proposed technique is promising as it successfully extracted the exact locations of eye blinking artifacts. The accuracy of the method (automatic detection) is 89.6% which represents a better estimate than that obtained by an extreme machine learning classifier.

eye blink artifact
sparse representation
support vector machine
pattern recognition
neural computation
Figure 1.
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