EOG artifact minimization using oblique projection corrected eigenvector decomposition.
In this paper, the authors propose an efficient algorithm to minimize the electrooculogram (EOG) artifacts in electroencephalogram (EEG). The approach uses the eigenvectors obtained from a learning process to initialize an oblique projection based blind source extraction (BSE) algorithm. It is used to extract the point source EOG artifacts. EEG data is subsequently reconstructed by a deflation method. The simulations with synthetic data illustrate that the BSE corrected algorithm is reliable and has better performance than the uncorrected eigenvector decomposition based method. The results of simulations with real EEG data confirms the effectiveness of our algorithm.
Zhou
Ziling
Z
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore. g0703135@nus.edu.sg
Puthusserypady
Sadasivan
S
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D004585:Electrooculography / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009474:Neurons / Q000502:physiology; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors