Related Experiment Video
Updated: Jun 17, 2026
10:35
Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
Published on: June 3, 2013
Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent
Camillo Porcaro1, Dirk Ostwald, Andrew P Bagshaw
1School of Psychology, University of Birmingham, Birmingham, UK. c.porcaro@bham.ac.uk
Neuroimage
|December 17, 2009
Summary
Functional Source Separation (FSS) significantly improves electroencephalography (EEG) data quality for combined EEG-fMRI studies. FSS outperforms Independent Component Analysis (ICA) in recovering single-trial evoked potentials, crucial for integrating these datasets.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- High-quality electroencephalography (EEG) data is essential for combined EEG-functional Magnetic Resonance Imaging (fMRI) studies, especially when analyzing single-trial (ST) variability.
- Independent Component Analysis (ICA) is a common method for removing MRI artifacts from EEG data, but it is a blind source separation technique.
- Integrating EEG and fMRI data requires robust methods for artifact removal and signal extraction.
Purpose of the Study:
- To introduce and evaluate Functional Source Separation (FSS) as an alternative to ICA for improving EEG data quality during concurrent EEG-fMRI recordings.
- To compare the performance of FSS against ICA and raw data in recovering single-trial evoked potentials.
- To assess the utility of FSS for integrating EEG and fMRI data by analyzing ST variability.
Main Methods:
- Functional Source Separation (FSS), an extension of ICA that incorporates prior knowledge of the signal of interest, was applied to EEG data.
- Visual evoked potentials (VEPs) were elicited using a reversing checkerboard stimulus in healthy subjects.
- Gradient and ballistocardiogram artifacts were removed using template subtraction, followed by ICA denoising and FSS.
- EEG data quality was assessed using metrics from average and ST data, and correlated with fMRI data.
Main Results:
- Both ICA and FSS improved EEG data quality compared to raw data.
- FSS consistently outperformed ICA in recovering high-quality single-trial evoked potentials.
- The FSS method demonstrated significant benefits for artifact removal and signal recovery in the context of concurrent EEG-fMRI.
Conclusions:
- Functional Source Separation (FSS) offers a superior approach to Independent Component Analysis (ICA) for enhancing EEG data quality in combined EEG-fMRI studies.
- FSS effectively recovers single-trial evoked potentials, which is critical for leveraging ST variability in multimodal neuroimaging.
- The findings support the use of FSS for more accurate and reliable integration of EEG and fMRI data.
