Zafar, R. and Qayyum, A. and Mumtaz, W. (2019) Automatic eye blink artifact removal for EEG based on a sparse coding technique for assessing major mental disorders. Journal of Integrative Neuroscience, 18 (3). pp. 217-229. ISSN 02196352
Full text not available from this repository.Abstract
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. ©2019 Zafar et al. Published by IMR Press. All rights reserved.
Item Type: | Article |
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Additional Information: | cited By 3 |
Uncontrolled Keywords: | adult; Article; artifact; automation; blinking; classifier; clinical article; controlled study; diagnostic accuracy; electroencephalogram; eyelid reflex; female; human; machine learning; major depression; male; mental patient; pattern recognition; priority journal; support vector machine; algorithm; blinking; electroencephalography; major depression; middle aged; procedures; signal processing, Adult; Algorithms; Artifacts; Blinking; Depressive Disorder, Major; Electroencephalography; Female; Humans; Male; Middle Aged; Signal Processing, Computer-Assisted |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 10 Nov 2023 03:25 |
Last Modified: | 10 Nov 2023 03:25 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/11346 |