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Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

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Robust EMG-fMRI artifact reduction for motion (FARM).

J N van der Meer¹, M A J Tijssen, L J Bour

¹Department of Neurology/Clinical Neurophysiology, Academic Medical Centre, Amsterdam, The Netherlands.

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology

|February 2, 2010

Summary

A new algorithm, FARM, effectively reduces motion artifacts in combined electromyography-functional magnetic resonance imaging (EMG-fMRI) studies. FARM improves EMG signal quality, especially at higher frequencies, enabling better analysis of limb movements and brain activity.

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Area of Science:

Neuroimaging
Biomedical Engineering
Signal Processing

Background:

Current methods struggle with motion artifacts in combined EMG-fMRI.
Existing techniques are insufficient for high-frequency EMG signals (>50 Hz).
Limb motion introduces significant volume- and slice-artifacts.

Purpose of the Study:

Introduce a novel algorithm, FARM, for reducing motion artifacts in EMG-fMRI.
Compare the efficacy of FARM against the standard FASTR method.
Enhance the quality of EMG data acquired during fMRI.

Main Methods:

Developed FARM, incorporating phase-shifting and motion-adaptive templates.
Applied low-frequency and volume-artifact removal before slice-artifact correction.
Collected EMG-fMRI data from a control subject with induced motion artifacts.

Main Results:

FARM effectively contains artifacts related to sudden wire position changes.
EMG power spectra demonstrate FARM's superior performance at higher frequencies.
Artifact reduction was improved compared to FASTR.

Conclusions:

FARM enhances EMG-fMRI data quality through advanced artifact reduction techniques.
Improved accuracy facilitates direct correlation of self-paced movements with brain activity.
This advancement aids research in movement disorders and motor control.