An automatic ocular artifacts removal method based on wavelet-enhanced canonical correlation analysis

Author

Zhao, Chunyu ; Qiu, Tianshuang

Author_Institution

Dept. of Biomed. Eng., Dalian Univ. of Technol., Dalian, China

fYear

2011

fDate

Aug. 30 2011-Sept. 3 2011

Firstpage

4191

Lastpage

4194

Abstract

In this paper, a new method for automatic ocular artifacts (OA) removal in EEG recordings is proposed based on wavelet-enhanced canonical correlation analysis (wCCA). Compared to three popular ocular artifacts removal methods, wCCA owns two advantages. First, there is no need to identify the artifact components by subjective visual inspection, because the first canonical components found by CCA for each dataset, also the most common component between the left and right hemisphere, are definitely related to artifacts. Second, quantitative evaluation of the corrected EEG signals demonstrates that wCCA removed the most ocular artifacts with minimal cerebral information loss.

Keywords

electroencephalography; neurophysiology; EEG recording; EEG signals; artifact components; automatic ocular artifact removal method; cerebral information loss; dataset; left hemisphere; right hemisphere; subjective visual inspection; wCCA; wavelet-enhanced canonical correlation analysis; Correlation; Electroencephalography; Electrooculography; Inspection; Visualization; Wavelet analysis; Wavelet transforms; Artifacts; Automation; Electroencephalography; Eye; Humans;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE

Conference_Location

Boston, MA

ISSN

1557-170X

Print_ISBN

978-1-4244-4121-1

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2011.6091040

Filename

6091040