Magneto- and Electro- encephalography (MEG/EEG)

1. Magneto- and Electro- encephalography (MEG/EEG) Neural-signals induced electromagnetic fields which are detectable outside of the brain Functional Neuroimaging Processing and analysis of sensor data Analyzes the functional purposes served by different regions of the brain To understand the neural mechanism of certain human behavior and neurological disorders Neural/Dipole source localization Current flow modeled by dipoles Given sensor measurements, determines the locations of the underlying neural activity The SAFFIRE Algorithm for Spatio-Temporal Reconstruction of MEG/EEG SignalsDr. Shannon Blunt, Tszping Chan sdblunt@ittc.ku.edu Overview Source AFFine Image REconstruction • Minimum mean square error approach • Incorporate linear underlying forward model • Leadfields matrix B • Transform dipole activity into MEG sensor signal data • Underdetermined model and extremely ill-conditioned • Filter formulation: • Iteratively update structured covariance matrix Rx • Implement filter in the affine-transformed domain • Matched filter initialization • Ensure convergence to true solution • Process multiple snapshots for SNR gain Impact Results - ASSR Example • Advantages • No prior statistical knowledge required • Unprecedented spatio-temporal resolution • Temporal signal correlation robustness • Powerful bio-signal processing tool • Successfully separated primary and secondary ASSR • Previously unsolved problem in brain imaging community • Patent-pending • Auditory Steady-State Response Red: Primary auditory dipole pair Green: Secondary auditory dipole pair Blue: Estimated dipole location Comparison - Mirrored Dipole Example