title: A real-time method to reduce ballistocardiogram artifacts from EEG during fMRI based on optimal basis sets (OBS).
abstract: BACKGROUND
  The simultaneous acquisition of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) provides both high temporal and spatial resolution when measuring brain activity. A real-time analysis during a simultaneous EEG-fMRI acquisition is essential when studying neurofeedback and conducting effective brain activity monitoring. However, the ballistocardiogram (BCG) artifacts which are induced by heartbeat-related electrode movements in an MRI scanner severely contaminate the EEG signals and hinder a reliable real-time analysis.
  
  
  METHODS
  The optimal basis sets (OBS) method is an effective candidate for removing BCG artifacts in a traditional offline EEG-fMRI analysis, but has yet to be applied to a real-time EEG-fMRI analysis. Here, a novel real-time technique based on OBS method (rtOBS) is proposed to remove BCG artifacts on a moment-to-moment basis. Real-time electrocardiogram R-peak detection procedure and sliding window OBS method were adopted.
  
  
  RESULTS
  A series of simulated data was constructed to verify the feasibility of the rtOBS technique. Furthermore, this method was applied to real EEG-fMRI data to remove BCG artifacts. The results of both simulated data and real EEG-fMRI data from eight healthy human subjects demonstrate the effectiveness of rtOBS in both the time and frequency domains.
  
  
  METHODS
  A comparison between rtOBS and real-time averaged artifact subtraction (rtAAS) was conducted. The results suggest the efficacy and advantage of rtOBS in the real-time removal of BCG artifacts.
  
  
  CONCLUSIONS
  In this study, a novel real-time OBS technique was proposed for the real-time removal of BCG artifacts. The proposed method was tested using simulated data and applied to real simultaneous EEG-fMRI data. The results suggest the effectiveness of this method.
authors.affiliation: College of Information Science and Technology, Beijing Normal University, 100875 Beijing, China; State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 100088 Beijing, China.
authors.forename: Xia
authors.initials: X
authors.lastname: Wu
authors.affiliation: College of Information Science and Technology, Beijing Normal University, 100875 Beijing, China.
authors.forename: Tong
authors.initials: T
authors.lastname: Wu
authors.affiliation: National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 100875 Beijing, China.
authors.forename: Zhichao
authors.initials: Z
authors.lastname: Zhan
authors.affiliation: College of Information Science and Technology, Beijing Normal University, 100875 Beijing, China.
authors.forename: Li
authors.initials: L
authors.lastname: Yao
authors.affiliation: Department of Psychology, Renmin University of China, 100872 Beijing, China. Electronic address: wenxiaotong@gmail.com.
authors.forename: Xiaotong
authors.initials: X
authors.lastname: Wen
mesh_terms: D016477:Artifacts; D001450:Ballistocardiography / Q000592:standards*; D004569:Electroencephalography / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*
keywords: Ballistocardiogram artifacts; Real-time optimal basis sets; Simultaneous EEG–fMRI