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Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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Gradient Artefact Correction and Evaluation of the EEG Recorded Simultaneously with fMRI Data Using Optimised
José L Ferreira1, Yan Wu1, René M H Besseling1
1Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands.
Journal of Medical Engineering
|July 23, 2016
Summary
We developed an optimized moving-average (OMA) filter to improve electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data quality by effectively removing gradient artifacts while preserving the EEG signal.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is crucial for neurocognitive research, integrating EEG's temporal resolution with fMRI's spatial resolution.
- A significant challenge is the gradient artifact in EEG data acquired during fMRI, which degrades signal quality.
Purpose of the Study:
- To introduce a novel and effective method for correcting gradient artifacts in simultaneous EEG-fMRI data.
- To improve the balance between artifact suppression and EEG signal preservation compared to existing methods.
Main Methods:
- Developed an optimized moving-average (OMA) filtering technique for gradient artifact correction.
- OMA iteratively applies a moving-average filter for artifact estimation and cancellation.
- The method effectively attenuates periodic artifact activity without requiring precise MRI trigger information.
Main Results:
- The OMA approach demonstrates superior performance over the established slice-average subtraction (AAS) method.
- Achieved a better balance between preserving the EEG signal and effectively suppressing gradient artifacts.
- Proposed a method to assess EEG signal preservation despite the stochastic nature of EEG data.
Conclusions:
- The OMA filtering technique offers an effective solution for gradient artifact correction in simultaneous EEG-fMRI.
- This method enhances the quality of EEG data acquired during fMRI, facilitating more reliable neurocognitive research.
- The proposed assessment method aids in validating the effectiveness of artifact correction techniques.
