Abstract

.Significance: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly.Aim: We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction.Approach: We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas.Results: We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes.Conclusions: We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.

Highlights

  • Electroencephalography (EEG) sensing is widely used for neuronal activity monitoring

  • The algorithm was implemented using Matlab (Mathworks Inc., Massachusetts), and the restricted maximum likelihood (ReML) part was adapted from the implementation in the NIRS Brain AnalyzIR toolbox.[33]

  • The codes for generating the forward models and simulating the results are available at https://github.com/ JiamingCao/NIRS-EEG

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Summary

Introduction

Electroencephalography (EEG) sensing is widely used for neuronal activity monitoring. Its benefit is the direct measurement of the electrical neuronal activities at high (∼millisecond) temporal resolution. The spatial resolution of EEG is low because the distance between the brain and the scalp acts as a spatial low-pass filter.[1] one has to solve a highly ill-posed inverse problem to reconstruct the neuronal sources.[2] The reconstructed source’s point spread can be unsatisfactorily large (typically on the order of few centimeters),[1] especially when precise source localization is required, e.g., localizing the seizure focus of epilepsy. Theoretical studies have shown that lower densities of EEG fundamentally limit its spatial resolution.[3]

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