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Artifact Handling in the MR-compatible Geodesic EEG System (GES) 400

Imaging Artifacts

Template Subtraction in Net Station software — During each head volume acquisition, both the gradient switching and radio frequency pulse pattern will produce strong artifacts that will obscure the EEG. However, the repetition time (TR) chosen for a particular scan protocol will provide this artifact with a specific temporal pattern.

Although imaging artifacts initially render the EEG completely unreadable, they do have characteristics that make them ideal targets for artifact correction. The gradient switching and radio frequency pulses occur within nanoseconds of each other across each volume acquisition, thereby creating a consistent artifact waveform profile with respect to each TR. As the MRI scanner sends a TR trigger to the EEG file, there is a clear method of identifying the onset and profile of the artifact. This promotes a relatively straightforward method of artifact correction — calculate an average of the imaging artifact waveform and subtract it from the simultaneously recorded EEG. The procedure is built into Net Station software and can be implemented either online or offline.

g00251 mr data screenshot for web

GES Clock Sync I/O — Since the EEG acquisition and MRI scanner use separate computers, their clocks are independent. In addition, the MRI scanner itself has a very high amplitude and rapid slew rate. Therefore, tiny amounts of jitter between the two clocks will result in the imaging artifact profile varying from one volume to the next. As this inconsistency reduces the efficacy of the average artifact subtraction method, it is not uncommon for additional steps to be introduced to the data cleaning workflow, such as signal interpolation and adaptive noise cancellation.

These limitations have led us to take a novel approach. We have created a situation where the EEG acquisition is effectively running the on the MRI system’s clock. This is done by introducing a phase-locked loop between the MRI and EEG system, which results in the jitter between the two clocks being reduced to a negligible amount (5-10 nanoseconds). In another example of how we closely work with our customers when developing our new products, we are licensing this synchronization technique from Dr. Mark Cohen from UCLA’s David Geffen School of Medicine. It is covered by US Patent No. 7,286,871. 

Field Isolation Containment System for EEG-MRI

Field Isolation Containment System (FICS) — Front-end filters within our FICS unit decrease the amplitude of the artifact prior to signal digitization. By the skillful combination of these two techniques we are then able to effectively remove the imaging artifact using the simple average artifact subtraction method. 


Cardioballistic Artifacts

Every time the heart beats, the resultant micromovement and blood flow of the head causes this periodic artifact.

ECG-based method in Net Station software — Balistocardiogram (BCG) artifacts are difficult to handle and remove for several reasons: 1) within each electrode, their amplitudes and duration vary between successive heartbeats, 2) between electrodes their amplitudes and pattern vary, 3) their energy distribution overlaps with the frequency of the EEG, and 4) they share similar morphology with certain EEG phenomena. There are a handful of techniques that have been proposed for handling BCG artifacts (see Groiller et al., 2007) and we currently recommend the technique of Niazy et al. (2005), implemented in the FMRIB EEGLAB plug-in. We have implemented this method within Net Station software to optimize workflow.

EEG-based pulse artifact detection in Net Station software — In Net Station 5.2 and later, the MR-compatible GES 400 systems include a new EEG-based method for detecting the pulse artifact (PA) with significant benefits over the standard ECG-based methods, based on Iannotti, et al. (2015). The method uses the anti-symmetric scalp voltage potential topography of the pulse artifact, captured by the EEG, to identify the artifacts. At least as accurate as ECG-based methods, and often more so, the EEG-based method can replace ECG-based methods, being especially useful in cases where it is difficult to obtain clean ECG data, and can also complement ECG-based artifact detection as a secondary measurement.