Blind separation of ballistocardiogram from EEG via short-and-long-term linear prediction filtering

In this paper the problem of removing ballistocardiogram (BCG) artifact from EEG signals is addressed. This kind of artifact appears in simultaneous EEG-fMRI recordings. We propose a new Blind source extraction method based on linear prediction technique. The proposed method is a joint short-and-long-term prediction (SLTP) strategy to extract the BCG sources. The main reason of using this technique is to jointly model the temporal structure (short-term prediction) of sources and exploiting the prior information of BCG sources (long-term prediction). The results of extensive experiments on both synthetic and real data confirm the strength of the proposed technique to effectively remove the BCG artifact.

[1]  Robert Turner,et al.  A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI , 2000, NeuroImage.

[2]  Andrzej Cichocki,et al.  Blind source extraction based on a linear predictor , 2007 .

[3]  F Kruggel,et al.  Recording of the event‐related potentials during functional MRI at 3.0 Tesla field strength , 2000, Magnetic resonance in medicine.

[4]  G. Srivastava,et al.  ICA-based procedures for removing ballistocardiogram artifacts from EEG data acquired in the MRI scanner , 2005, NeuroImage.

[5]  Emery N. Brown,et al.  Motion and Ballistocardiogram Artifact Removal for Interleaved Recording of EEG and EPs during MRI , 2002, NeuroImage.

[6]  Louis Lemieux,et al.  Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction , 1998, NeuroImage.

[7]  John G. McWhirter,et al.  Removal of ballistocardiogram artifacts from EEG/fMRI data using cyclostationary source extraction method , 2010 .

[8]  Rami K. Niazy,et al.  Removal of FMRI environment artifacts from EEG data using optimal basis sets , 2005, NeuroImage.

[9]  Christophe Phillips,et al.  Rejection of pulse related artefact (PRA) from continuous electroencephalographic (EEG) time series recorded during functional magnetic resonance imaging (fMRI) using constraint independent component analysis (cICA) , 2009, NeuroImage.

[10]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[11]  Gian Luca Romani,et al.  Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis , 2007, NeuroImage.

[12]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[13]  Andrzej Cichocki,et al.  Removal of ballistocardiogram artifacts from simultaneously recorded EEG and fMRI data using independent component analysis , 2006, IEEE Transactions on Biomedical Engineering.

[14]  Andrzej Cichocki,et al.  On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Sources Using Second Order Statistics , 2000, Neural Processing Letters.

[15]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[16]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.