A new multimodal cortical source imaging algorithm for integrating simultaneously recorded EEG and MEG

Abstract

In this study, we propose a new cortical source imaging algorithm for integrating simultaneously recorded electroencephalography (EEG) and magnetoencephalography (MEG), which takes into account the different sensitivity characteristics of the two modalities with respect to cortical source orientations. It is generally accepted that MEG can record neuronal electrical activities with higher spatial resolution than EEG because magnetic field is less affected by the inhomogeneous conductivity profile of the human head than electric field. Nevertheless, it is also well known that MEG cannot reliably detect neuronal sources with radial orientation, whereas EEG is relatively less dependent on the source orientations than MEG. However, this intrinsic difference has not previously been taken into account in the integrative cortical source imaging using simultaneously recorded EEG and MEG data. To consider the different directional sensitivities of the two modalities, we conceptually decomposed the sensor and source spaces into radial and tangential components and developed an explicit formulation described as a general type of weighted minimum norm estimation. Using extensive computer simulations, we demonstrated that our proposed algorithm substantially enhances localization accuracy over almost the entire cortical surface compared to that of the conventional integration algorithm and results in a significant improvement especially when either a radial or tangential source component is dominant.

Keywords:

• cortical source imaging
• inverse problem
• multimodal neuroimaging
• electroencephalography
• magnetoencephalography

Acknowledgements

This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2010-0015604) and in part by the research fund of Hanyang University (HY-2011 -N).