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.

Automatic and Direct Identification of Blink Components from Scalp EEG / Wanzeng, Kong; Zhanpeng, Zhou; Sanqing, Hu; Jianhai, Zhang; Babiloni, Fabio; Guojun, Dai. - In: SENSORS. - ISSN 1424-8220. - STAMPA. - 13:8(2013), pp. 10783-10801. [10.3390/s130810783]

Automatic and Direct Identification of Blink Components from Scalp EEG

BABILONI, Fabio;
2013

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.
2013
independent component analysis; scalp eeg; eye blink artifact; correlation
01 Pubblicazione su rivista::01a Articolo in rivista
Automatic and Direct Identification of Blink Components from Scalp EEG / Wanzeng, Kong; Zhanpeng, Zhou; Sanqing, Hu; Jianhai, Zhang; Babiloni, Fabio; Guojun, Dai. - In: SENSORS. - ISSN 1424-8220. - STAMPA. - 13:8(2013), pp. 10783-10801. [10.3390/s130810783]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/521626
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