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Improvements to Simultaneous Electroencephalography – functional Magnetic Resonance Imaging and Electroencephalographic Source Localization


Both the method of simultaneous electroencephalography – functional magnetic resonance imaging (EEG-fMRI) and the method of electroencephalographic source localization are opening up new understandings in neuroscience, but both are also technically very challenging. Simultaneous EEG-fMRI combines the strengths of each modality: the temporal resolution of electroencephalography (EEG) with the spatial resolution of functional magnetic resonance imaging (fMRI). EEG source localization provides a non-invasive means of locating in the brain the sources of electrical signals measured on the surface of the head. The potential for these methods is not limited to research neuroscience; each has much to offer in fields like clinical neurology and psychiatry. In clinical neurology, for example, both methods have been used in the location of epileptic foci. Simultaneous EEG-fMRI is technically very challenging as each modality serves as a source of artifact and safety concerns for the other. Two contaminants prominent in EEG acquired in the MRI environment are the gradient artifact (GA) and the ballistocardiogram artifact (BCG). The GA occurs only during an active MRI acquisition, while the BCG is always present, as its origins arise from the cardiovascular system of the subject. The current method to remove the GA from the EEG recording is called moving windowed average template subtraction (MWATS), the windows of which are time-locked to the repetition time of the MR acquisition. The problem with MWATS is that there are a number of options on how to implement it and little consensus or consistency in the field on how it is done. Current methods of removing the BCG artifact from the EEG recording all rely on establishing its timing based upon a simultaneously acquired electrocardiogram (ECG) signal. One issue with using the ECG signal is that the differential method for acquiring it implemented by the manufactures of the MR compatible EEG systems has a high rate of failure.

Source localization is technically very challenging in part because it relies on multiple measurements for its solution. Two of the measurements required for accurate source location are the measurement of the EEG electrode positions with respect to the underlying anatomy and the measurement of the underlying anatomy itself. At present, these measurements are made separately: the underlying anatomy is measured with an anatomical MRI scan, while the electrode positions are measured with either an electromagnetic digitization device or a photogrammetry system, neither of which is MR compatible. As such the two measurements must be registered to one another, typically through the location of fiducial markers on the surface of the head in both of the measurements. By aligning the markers, the two measurements can be brought into register. The issues with this technique are two fold. First, aligning the separate measurements introduces a potential source of error. Second, both of the current methods for measuring the electrode positions are very time consuming. This thesis focuses in robust solutions to these issues.

Specifically what follows is: 1) an analysis of the consequences of all the principal options for implementing the moving windowed average template subtraction; 2) a method to eliminate the need of the separately recorded ECG signal for establishing the timing of the BCG artifact; and 3) a method for automatically locating and identifying EEG electrodes in an anatomical MRI scan. The analysis of the MWATS method permits more informed decisions to be made in its implementation. The method to eliminate the need for the ECG signal is to both simplify the experimental setup and to make the EEG-fMRI recording more robust. Automatically locating and identifying EEG electrodes in an anatomical MRI scan eliminates both a measurement step and a registration step. The elimination of the registration step eliminates a potential source of error. In addition, the combination of the elimination of the measurement step and the automatic way in which the algorithm progresses greatly reduces the time required for this process.

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