Electromagnetic Brain Source Imaging by Means of a Robust Minimum Variance Beamformer
We propose a technique called robust minimum variance beamformer (RMVB) which enables adaptive beamformers to remain robust against model non-idealities in the lead-field matrix. RMVB yields robustness by quantifying different model uncertainties as hyper-dimensional ellipsoids. These uncertainty regions, which can be estimated empirically by building several forward models, are then directly incorporated in the equations to estimate the spatial-filter weights which will be subsequently used for imaging.
Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach
We present an electrophysiological connectome (eConnectome) approach to study underlying brain networks in a noninvasive manner. This approach was directly tested by estimating epileptic networks from EEG/MEG measurements in patients suffering from focal epilepsy. The results obtained from the proposed approach were consistent with invasive clinical findings, in these patients.
Electrophysiological Source Imaging of Brain Networks Perturbed by Low-Intensity Transcranial Focused Ultrasound
We report an experimental investigation to noninvasively detect electrophysiological response induced by low-intensity transcranial focused ultrasound (tFUS) in an in vivo animal model, and perform electrophysiological source imaging (ESI) of tFUS-induced brain activity from noninvasive scalp EEG recordings. Neural activation has been observed following low-intensity tFUS, for various ultrasound intensities and sonication durations.