Source modelling of ElectroCorticoGraphy data: stability analysis and spatial filtering
ElectroCOrticoGraphy (ECoG) is an invasive neuroimaging technique that measures electrical potentials produced by brain currents via an electrode grid implanted on the cortical surface.
A full interpretation of ECoG data is difficult because it requires solving the inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible of the recorded ECoG signals, which is ill-posed. Only in the last few years novel computational methods to solve this inverse problem have been developed.
This study describes a beamformer method for ECoG source modeling. First, we extended the OpenMEEG software with a new method to estimate the lead-field matrix which maps the neural currents onto the sensors space. We further conducted an analysis of the numerical stability of the ECoG inverse problem by computing the condition number of the lead-field matrix for different configurations of the electrodes grid. Finally, we localized sources via a Linear Constraint Minimum Variance (LCMV) beamformer method applied to both synthetic and real data.