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Automatic and Direct Identification of Blink Components from Scalp EEG

College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Department of Physiology and Pharmacology, University of Rome "Sapienza", Rome 00185, Italy
IRCCS Fondazione Santa Lucia, via Ardeatina 306, Rome 00179, Italy
Author to whom correspondence should be addressed.
Sensors 2013, 13(8), 10783-10801;
Received: 15 July 2013 / Revised: 8 August 2013 / Accepted: 12 August 2013 / Published: 16 August 2013
(This article belongs to the Special Issue Biomedical Sensors and Systems)
PDF [2899 KB, uploaded 21 June 2014]


Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn’t need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects. View Full-Text
Keywords: scalp EEG; correlation; eye blink artifact; independent component analysis (ICA); identify scalp EEG; correlation; eye blink artifact; independent component analysis (ICA); identify
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Kong, W.; Zhou, Z.; Hu, S.; Zhang, J.; Babiloni, F.; Dai, G. Automatic and Direct Identification of Blink Components from Scalp EEG. Sensors 2013, 13, 10783-10801.

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