Automatic EEG artifact removal: a weighted support vector machine approach with error correction.
An automatic electroencephalogram (EEG) artifact removal method is presented in this paper. Compared to past methods, it has two unique features: 1) a weighted version of support vector machine formulation that handles the inherent unbalanced nature of component classification and 2) the ability to accommodate structural information typically found in component classification. The advantages of the proposed method are demonstrated on real-life EEG recordings with comparisons made to several benchmark methods. Results show that the proposed method is preferable to the other methods in the context of artifact removal by achieving a better tradeoff between removing artifacts and preserving inherent brain activities. Qualitative evaluation of the reconstructed EEG epochs also demonstrates that after artifact removal inherent brain activities are largely preserved.
Shao
Shi-Yun
SY
Department of Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore. shao@nus.edu.sg
Shen
Kai-Quan
KQ
Ong
Chong Jin
CJ
Wilder-Smith
Einar P V
EP
Li
Xiao-Ping
XP
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001767:Blinking; D003951:Diagnostic Errors; D004562:Electrocardiography; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008955:Models, Cardiovascular; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted