Elham Barzegaran1, Sebastian Bosse2, Peter J Kohler3, Anthony M Norcia4. 1. Department of Psychology, Jordan Hall, Building 420, Stanford University, Stanford, CA 94305, USA. Electronic address: e.barzegaran@gmail.com. 2. Department of Video Coding & Analytics, Fraunhofer Heinrich Hertz Institute, 10587 Berlin, Germany. Electronic address: sebastian.bosse@hhi.fraunhofer.de. 3. Department of Psychology, Jordan Hall, Building 420, Stanford University, Stanford, CA 94305, USA; Department of Psychology and Centre for Vision Research, Core Member, Vision: Science to Applications (VISTA), York University, 4700 Keele St., Toronto, ON, M3J 1P3, Canada. Electronic address: pjkohl3r@gmail.com. 4. Department of Psychology, Jordan Hall, Building 420, Stanford University, Stanford, CA 94305, USA. Electronic address: amnorcia@stanford.edu.
Abstract
BACKGROUND: Electroencephalography (EEG) is widely used to investigate human brain function. Simulation studies are essential for assessing the validity of EEG analysis methods and the interpretability of results. NEW METHOD: Here we present a simulation environment for generating EEG data by embedding biologically plausible signal and noise into MRI-based forward models that incorporate individual-subject variability in structure and function. RESULTS: The package includes pipelines for the evaluation and validation of EEG analysis tools for source estimation, functional connectivity, and spatial filtering. EEG dynamics can be simulated using realistic noise and signal models with user specifiable signal-to-noise ratio (SNR). We also provide a set of quantitative metrics tailored to source estimation, connectivity and spatial filtering applications. COMPARISON WITH EXISTING METHOD(S): We provide a larger set of forward solutions for individual MRI-based head models than has been available previously. These head models are surface-based and include two sets of regions-of-interest (ROIs) that have been brought into registration with the brain of each individual using surface-based alignment - one from a whole brain and the other from a visual cortex atlas. We derive a realistic model of noise by fitting different model components to measured resting state EEG. We also provide a set of quantitative metrics for evaluating source-localization, functional connectivity and spatial filtering methods. CONCLUSIONS: The inclusion of a larger number of individual head-models, combined with surface-atlas based labeling of ROIs and plausible models of signal and noise, allows for simulation of EEG data with greater realism than previous packages.
BACKGROUND: Electroencephalography (EEG) is widely used to investigate human brain function. Simulation studies are essential for assessing the validity of EEG analysis methods and the interpretability of results. NEW METHOD: Here we present a simulation environment for generating EEG data by embedding biologically plausible signal and noise into MRI-based forward models that incorporate individual-subject variability in structure and function. RESULTS: The package includes pipelines for the evaluation and validation of EEG analysis tools for source estimation, functional connectivity, and spatial filtering. EEG dynamics can be simulated using realistic noise and signal models with user specifiable signal-to-noise ratio (SNR). We also provide a set of quantitative metrics tailored to source estimation, connectivity and spatial filtering applications. COMPARISON WITH EXISTING METHOD(S): We provide a larger set of forward solutions for individual MRI-based head models than has been available previously. These head models are surface-based and include two sets of regions-of-interest (ROIs) that have been brought into registration with the brain of each individual using surface-based alignment - one from a whole brain and the other from a visual cortex atlas. We derive a realistic model of noise by fitting different model components to measured resting state EEG. We also provide a set of quantitative metrics for evaluating source-localization, functional connectivity and spatial filtering methods. CONCLUSIONS: The inclusion of a larger number of individual head-models, combined with surface-atlas based labeling of ROIs and plausible models of signal and noise, allows for simulation of EEG data with greater realism than previous packages.
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