Evaluation of a Method of Removing Head Movement Artifact from EEG by Independent Component Analysis and Filtering

Abstract

Artifacts that contaminate electroencephalography (EEG) signals make it diffcult to analyze EEG. The aim of this study was to removal artifacts on EEG, especially those caused by motion, to measure EEG in unconstrained situations. In a pre-vious study, head movements were detected by an accelerometer, and motion artifact components were separated from the re-corded EEG by independent component analysis (ICA). This method is effective for reducing the effect of artifacts, but has a risk that EEG components are also removed. In this paper, we introduce an improved artifact removal method based on ICA and ltering. EEG were decomposed by ICA, and a Pearsons correlation coeffcient was calculated between each independent com-ponent and each hybrid accelerometer value to distinguish artifact components. Artifact components were then high-pass l-tered. In this study, subjects were instructed to move their heads randomly, while keeping their eyes closed. The previous meth-od was adapted using 1, 2, 3, 5 and 10 s to nd the most suitable epoch to minimize the mean absolute amplitude of the cleaned EEG. Then, using this epoch, the proposed method was compared with the previous method by frequency analysis. Low fre-quency power (0.1–3 Hz) was normalized to unity because most power caused by motion artifacts exists in the low power band. If the normalized theta (4–8 Hz), alpha (8–13 Hz) and beta (13–40 Hz) powers of cleaned EEG are higher than that of raw EEG, this indicates that the effect of motion artifacts is small and EEG components are retained. The results obtained from theta and alpha power comparison showed that the proposed method performed better than the previous method. This result suggests that the proposed artifact removal method is more effective to reduce the effect of artifacts while retaining the EEG components.