Automatic and Direct Identification of Blink Components from Scalp EEG

Sensors

Wanzeng Kong¹  Zhanpeng Zhou¹  Sanqing Hu¹  Jianhai Zhang¹  Fabio Babiloni² 

[1] College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China; E-Mails:;Department of Physiology and Pharmacology, University of Rome “Sapienza”, Rome 00185, Italy; E-Mail:

关键词: scalp EEG;    correlation;    eye blink artifact;    independent component analysis (ICA);    identify;   

DOI  :  10.3390/s130810783

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.