Automatic and direct identification of blink components from scalp EEG.

Wanzeng Kong, Zhanpeng Zhou, Sanqing Hu, Jianhai Zhang, Fabio Babiloni, Guojun Dai

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)10783-10801
Number of pages19
JournalSensors (Switzerland)
Volume13
Issue number8
Publication statusPublished - 2013

ASJC Scopus subject areas

  • Medicine(all)

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  • Cite this

    Kong, W., Zhou, Z., Hu, S., Zhang, J., Babiloni, F., & Dai, G. (2013). Automatic and direct identification of blink components from scalp EEG. Sensors (Switzerland), 13(8), 10783-10801.