Background. Electrocorticography (ECoG) measures the distribution of electrical potentials by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data 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]. Methods. This study [2] addresses the ECoG source modelling using a beamformer method. We computed the lead-field matrix which maps the neural currents onto the sensors space by a novel routine provided by the OpenMEEG framework [3]. The ECoG source-modeling problem requires to invert this matrix by means of a regularization method which reduces its intrinsic numerical instability: we performed an analysis of the condition number of the lead-field matrix for different configurations of the electrodes grid. Finally, we provided quantitative results for source modeling using a Linear Constraint Minimum Variance (LCMV) beamformer [4]. The validation of the effectiveness of beamforming in ECoG was performed both with synthetic data and with experimental data recorded during a rapid visual categorization task. Results. For all considered grids the condition number indicates that the ECoG inverse problem is mildly ill-conditioned. For realistic SNR we found a good performance of the LCMV algorithm for both localization and waveforms reconstruction. The flow of information reconstructed by analyzing real data seems consistent with both invasive monkey electrophysiology studies and non-invasive (MEG and fMRI) human studies. References: 1. Dumpelmann et al., (2012), Human brain mapping, 33(5), 1172-1188 2. Pascarella et al. (2016), Journal of Neuroscience Methods, 263(5), 134-144 3. Kybic et al., (2005), Medical Imaging, IEEE Transactions on, 24(1), 12-28 4. Van Veen et al., (1997), Biomedical Engineering, IEEE Transactions on, 44(9), 867-880
Source modelling of ECoG data: stability analysis and spatial filtering
Annalisa Pascarella;
2016
Abstract
Background. Electrocorticography (ECoG) measures the distribution of electrical potentials by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data 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]. Methods. This study [2] addresses the ECoG source modelling using a beamformer method. We computed the lead-field matrix which maps the neural currents onto the sensors space by a novel routine provided by the OpenMEEG framework [3]. The ECoG source-modeling problem requires to invert this matrix by means of a regularization method which reduces its intrinsic numerical instability: we performed an analysis of the condition number of the lead-field matrix for different configurations of the electrodes grid. Finally, we provided quantitative results for source modeling using a Linear Constraint Minimum Variance (LCMV) beamformer [4]. The validation of the effectiveness of beamforming in ECoG was performed both with synthetic data and with experimental data recorded during a rapid visual categorization task. Results. For all considered grids the condition number indicates that the ECoG inverse problem is mildly ill-conditioned. For realistic SNR we found a good performance of the LCMV algorithm for both localization and waveforms reconstruction. The flow of information reconstructed by analyzing real data seems consistent with both invasive monkey electrophysiology studies and non-invasive (MEG and fMRI) human studies. References: 1. Dumpelmann et al., (2012), Human brain mapping, 33(5), 1172-1188 2. Pascarella et al. (2016), Journal of Neuroscience Methods, 263(5), 134-144 3. Kybic et al., (2005), Medical Imaging, IEEE Transactions on, 24(1), 12-28 4. Van Veen et al., (1997), Biomedical Engineering, IEEE Transactions on, 44(9), 867-880I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
