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Removing ballistocardiogram artifact from EEG using short- and long-term linear predictor
Saideh Ferdowsi1, Saeid Sanei, Vahid Abolghasemi
1Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK. s.ferdowsi@surrey.ac.uk
IEEE Transactions on Bio-Medical Engineering
|February 6, 2013
Summary
A new method called SLTP-BSE removes ballistocardiogram (BCG) artifact from electroencephalogram (EEG) signals. This technique effectively cleans EEG data for better analysis, outperforming existing methods.
Area of Science:
- Neuroscience
- Signal Processing
- Biomedical Engineering
Background:
- Ballistocardiogram (BCG) artifact contaminates electroencephalogram (EEG) signals during simultaneous functional magnetic resonance imaging (fMRI).
- Accurate artifact removal is crucial for reliable EEG analysis in fMRI studies.
Purpose of the Study:
- To introduce a novel semi-blind source extraction method for BCG artifact removal from EEG.
- To enhance the quality of EEG data recorded during fMRI.
Main Methods:
- Developed a semi-blind source extraction algorithm named SLTP-BSE (short- and long-term prediction blind source extraction).
- Utilized a linear prediction technique with a cost function based on joint short- and long-term prediction.
- Modeled temporal structures and incorporated prior information of BCG sources for optimization.
Main Results:
- The SLTP-BSE method successfully removed BCG artifacts from both synthetic and real EEG data.
- The technique preserved task-related components within the EEG signals.
- Comparative analysis demonstrated the superiority of SLTP-BSE over established BCG removal methods.
Conclusions:
- SLTP-BSE is an effective and superior method for removing BCG artifacts from EEG signals.
- The proposed approach offers improved EEG data quality for fMRI research.
