Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/59341
Title: Removing eye blink artefacts from EEG—A single-channel physiology-based method
Contributor(s): Zhang, Shenghuan (author); McIntosh, Julia (author); Shadli, Shabah M  (author)orcid ; Neo, Phoebe S-H (author); Huang, Zhiyi (author); McNaughton, Neil (author)
Publication Date: 2017
DOI: 10.1016/j.jneumeth.2017.08.031
Handle Link: https://hdl.handle.net/1959.11/59341
Abstract: 

Background: EEG signals are often contaminated with artefacts, particularly with large signals generated by eye blinks. Deletion of artefact can lose valuable data. Current methods of removing the eye blink component to leave residual EEG, such as blind source component removal, require multichannel recording, are computationally intensive, and can alter the original EEG signal.

New method: Here we describe a novel single-channel method using a model based on the ballistic physiological components of the eye blink. This removes the blink component, leaving uncontaminated EEG largely unchanged. Processing time allows its use in real-time applications such as neurofeedback training.

Results: Blink removal had a success rate of over 90% recovered variance of original EEG when removing synthesised eye blink components. Fronto-lateral sites were poorer (∼80%) than most other sites (92–96%), with poor fronto-polar results (67%).

Comparisons with existing methods: When compared with three popular independent component analysis (ICA) methods, our method was only slightly (1%) better at frontal midline sites but significantly (>20%) better at lateral sites with an overall advantage of ~10%.

Conclusions: With few recording channels and real-time processing, our method shows clear advantages over ICA for removing eye blinks. It should be particularly suited for use in portable brain-computerinterfaces and in neurofeedback training.

Publication Type: Journal Article
Source of Publication: Journal of Neuroscience Methods, 291(1), p. 213-220
Publisher: Elsevier BV
Place of Publication: The Netherlands
ISSN: 1872-678X
0165-0270
Fields of Research (FoR) 2020: 4203 Health services and systems
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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