Source modelling of ElectroCorticoGraphy (ECoG) data: stability analysis and spatial filtering

Electrocorticography (ECoG) is a neurophysiological modality that measures the distribution of electrical potentials, associated with either spontaneous or evoked neural activity, by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data, however, requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible for the recorded signals. Only in the last few years some methods have been proposed to solve this inverse problem [1]. This study addresses the ECoG source modelling using a beamformer method. First, we compute the lead-field matrix which maps the neural currents onto the sensors space: a novel routine for the computation of the lead-field matrix, based on the tools provided by the OpenMEEG framework, was used [2]. The ECoG source-modeling problem requires to invert this matrix by means of a regularization method which reduces its intrinsic numerical instability; thus, we perform an analysis of the condition number of the lead-field matrix which provides quantitative information on the numerical instability of the problem, independently of the kind of inversion algorithm applied. Finally, we provide quantitative results for source modeling using a Linear Constraint Minimum Variance (LCMV) beamformer. The validation of the effectiveness of beamforming in ECoG is performed both with synthetic data and with experimental data recorded during a rapid visual categorization task.