| Home | E-Submission | Sitemap | Editorial Office |  
Ann Clin Neurophysiol > Volume 3(1); 2001 > Article
Ann Clin Neurophysiol. 2001; 3(1): 26-30.
Eyeball Movements Removal in EEG by independent Component Analysis
Yong-Soo Shim, Seong-Ho Choi, and Il-Keun Lee
Copyright © 2001 The Korean Society of Clinical Neurophysiology
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Purpose : Eyeball movement is one of the main artifacts in EEG. A new approach to the removal of these artifacts is presented using independent component analysis(ICA). This technique is a signal-processing algorithm to separate independent sources from unknown mixed signals. This study was performed to show that ICA is a useful method for the separation of EEG components with little data deformity. Methods : 12 sets of 10 sec digital EEG data including eye opening and closure were obtained using international 10~20 system scalp electrodes. ICA with 18 tracings of double banana bipolar montage was performed. Among obtained 18 independent components, two components, which were thought to be eyeball movements were removed. Other 16 components were reconstructed into original bipolar montage. Power spectral analysis of EEGs before and after ICA was done and compared statistically. Total 12 pairs of data were compared by visual inspection and relative power comparison. Results : Waveforms of each pair looked alike by visual inspection. Means of relative before and after ICA were 29.16% vs. 28.27%, 12.12% vs. 12.41%, 10.55% vs. 19.52%, and 19.33% vs. 18.33% for alpha, beta, theta, and delta, respectively. These values were statistically same before and after ICA. Conclusions : We found little data deformity after ICA and it was possible to isolate eyeball movements in EEG recordings. Many other components of EEG could be selectively separated using ICA.
Key words: Independent component analysis(ICA), EEG
PDF Links  PDF Links
Download Citation  Download Citation
Related article
Editorial Office
Department of Neurology, Seoul National University (SNU) College of Medicine
SMG-SNU Boramae Medical Center
20 Boramaero-5-Gil, Dongjak-Gu, Seoul 07061, Republic of Korea
TEL : +82-2-2266-7238    FAX : +82-2-831-2826   E-mail: acn.journal@e-acn.org
About |  Browse Articles |  Current Issue |  For Authors and Reviewers
Copyright © The Korean Society of Clinical Neurophysiology.           Developed in M2PI