Evaluating the efficacy of an automated procedure for EEG artifact removal.
Electroencephalography (EEG) signals are often contaminated with artifacts arising from many sources such as those with ocular and muscular origins. Artifact removal techniques often rely on the experience of the EEG technician to detect these artifact components for removal. This paper presents the results comparing an automated procedure (AT) against visually (VT) choosing artifactual components for removal, using second order blind identification (SOBI) and canonical correlation analyses. The results show that the resulting EEG signal after artifact removal for the AT and VT were comparable using a technique that measures the variance amongst electrodes and spectral energy. The AT technique is objective, faster and easier to use, and shown here to be comparable to the standard technique of visually detecting artifact components.
Tran
Yvonne
Y
Key University Research Centre in Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney.
Thuraisingham
Ranjit A
RA
Craig
Ashley
A
Nguyen
Hung
H
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity