Reduced-rank neural activity index for EEG/MEG multi-source localization

• Main Authors: Piotrowski, T., Gutierrez, D., Yamada, I., Zygierewicz, J.
• Format: Conference Proceeding
• Language: English
• Subjects: beamforming; Brain modeling; Covariance matrices; dipole source localization; Electroencephalography; Machine assisted indexing; magnetoencephalography; MV-PURE estimator; Position measurement; reduced-rank signal processing; Signal to noise ratio
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP.2014.6854495

We consider the problem of electroencephalography (EEG) and magnetoencephalography (MEG) source localization using beamforming techniques. Specifically, we propose a reduced-rank extension of the recently derived multi-source activity index (MAI), which itself is an extension of the classical neural activity index to the multi-source case. We show that, for uncorrelated dipole sources and any nonzero rank constraint, the proposed reduced-rank multi-source activity index (RR-MAI) achieves the global maximum when evaluated at the true source positions. Therefore, the RR-MAI can be used to localize multiple sources simultaneously. Furthermore, we propose another version of the RR-MAI which can be seen as a natural generalization of the proposed index to arbitrarily correlated sources. We present a series of numerical simulations showing that the RR-MAI can achieve a more precise source localization than the full-rank MAI in the case when the EEG/MEG forward model becomes ill-conditioned, which in our settings corresponds to the case of closely positioned sources and low signal-to-noise ratio.