This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.OverviewUnderstanding the neural basis of human brain function requires knowledge about the spatial and temporal aspects of information processing. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) represent complementary brain imaging techniques in terms of their spatial and temporal resolution. The long-term goal of our research is to advance the use of fMRI and EEG for understanding the spatial and temporal dynamics of sensory and cognitive processes underlying brain function. In collaboration with colleagues in the Department of Neurology we are also extending our advanced signal processing methods to localize seizure foci in patients with epilepsy. Research ProblemThe main advantage of acquiring EEG and fMRI data simultaneously is that the two types of data reflect the same neuronal process. However, simultaneously recording results in contamination of EEG measurements due to the static and gradient magnetic fields of MRI scanner. Therefore, these artifacts need to be removed before the full potential of simultaneous of EEG and fMRI recordings (hereafter EEG-fMRI) can be fully realized. We propose to develop, test and validate procedures for artifact removal in EEG data acquired under the MRI scanner. The proposed studies will contribute important new information on optimal EEG-fMRI recording and analysis techniques, and thereby facilitate future multimodal imaging studies of brain function and dysfunction.
Specific AimsTwo kinds of artifacts contaminate EEG data acquired during fMRI viz. gradient and ballistocardiogram (BCG) artifacts. We propose to develop advanced signal processing algorithms to remove both the artifacts from the EEG data. The main aims of the proposal are:(1) To develop and test advanced signal processing algorithms for reduction of gradient and BCG artifacts in simultaneous EEG-fMRI recordings at 3T. (2) To test procedures developed in (1) for recovering task-relevant brain activations in simultaneous EEG-fMRI recordings at 3T.MethodsGradient artifact removalThe gradient artifacts are caused by electromotive force induction on the EEG leads due to the rapidly switching magnetic field gradients during fMRI acquisition. These are periodic artifacts with multiple spectral lines in the Fourier spectrum, whose fundamental frequency is governed by the scan parameters like repetition time (TR) and number of slices. The standard deviation of these artifacts is about 30-50 times as large as that of the EEG signal and hence the Signal to Artifact Ratio (SAR) is negative: -20 to -30 dB. A common approach in reducing this artifact is average artifact subtraction (AAS). A template for artifact is formed by averaging the contaminated data across time periods of one TR and is then subtracted from the data. This is based on the assumption that the artifact is similar across the time segments. However, due to asynchronous EEG and fMRI clocks, there will be a jitter in artifacts across different time segments and result in some residual noise even after the template subtraction. We propose a new method wherein we use wavelet based band pass filtering to remove sub-bands wherein there is no EEG information followed by a temporal independent component analysis (ICA) based subspace method to remove the artifacts. We test this procedure by simulating and adding gradient artifacts to EEG data. The method is verified by comparing the power spectra of clean and artifact filtered EEG data and also by extracting and comparing the evoked response potentials (ERP) from both the signals.BCG artifact removalBCG artifacts are a consequence of electromotive force (EMF) produced on the EEG electrodes due to small head movements, such as those caused by cardiac pulsation, inside the scanner magnetic field. The cardiac pulse generates artifacts with amplitudes considerably larger than EEG signal fluctuations. In addition, there is considerable variation in the artifact shape, amplitude, and scale over time. It is therefore important to develop methods for identifying and removing these artifacts in a robust manner. The group has already developed an ICA based method to remove BCG artifacts. The current method identifies noise related components either by visual inspection or correlation based methods. We propose to extend this work by introducing novel techniques for discriminating between noisy and meaningful components.
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