Removal of ballistocardiogram artifacts from simultaneously recorded EEG and fMRI data using independent component analysis.
Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been studied to identify areas related to EEG events. EEG data recorded in the magnetic resonance (MR) scanner with MR imaging is suffered from two specific artifacts, imaging artifact, and ballistocardiogram (BCG). In this paper, we focus on BCG. In preceding studies, average subtraction was often used for this purpose. However, average subtraction requires an assumption that BCG waveforms are precisely periodic, which seems unrealistic because BCG is a biomedical artifact. We propose the application of independent component analysis (ICA) with a postprocessing of high-pass filtering for the removal of BCG. With this approach, it is not necessary to assume that the BCG waveform is periodic. Empirically, we show that our proposed method removes BCG artifacts as well as does the average subtraction method. Power spectral density analysis of the two approaches shows that, with ICA, distortion of recovered EEG data is also as small as that associated with the average subtraction approach. We also propose a hypothesis for how head movement causes BCGs and show why ICA can remove BCG artifacts arising from this source.
Nakamura
Wakako
W
Research and Education Center for Brain Science, Hokkaido University, Kital 5 Nishi 7, Kita-ku, Sapporo 060-8638, Japan. wakako@riko.shimane-u.ac.jp
Anami
Kimitaka
K
Mori
Takeyuki
T
Saitoh
Osamu
O
Cichocki
Andrzej
A
Amari
Shun-ichi
S
D000465:Algorithms; D016477:Artifacts; D001450:Ballistocardiography / Q000379:methods*; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity