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Improved Back-Projection Cortical Potential Imaging by Multi-resolution Optimization Technique

  • Dror Haor
  • Roman Joffe
  • Reuven Shavit
  • Ziv Peremen
  • Yaki Stern
  • Amir B. Geva
Original Paper

Abstract

Electroencephalogram (EEG) has evolved to be a well-established tool for imaging brain activity. This progress is mainly due to the development of high-resolution (HR) EEG methods. One class of HR-EEG is the cortical potential imaging (CPI), which aims to estimate the potential distribution on the cortical surface, which is much more informative than EEG. Even though these methods exhibit good performance, most of them have inherent inaccuracies that originate from their operating principles that constrain the solution or require a complex calculation process. The back-projection CPI (BP-CPI) method is relatively new and has the advantage of being constraint-free and computation inexpensive. The method has shown relatively good accuracy, which is necessary to become a clinical tool. However, better performance must be achieved. In the present study, two improvements are proposed. Both are embedded as adjacent stages to the BP-CPI and are based on the multi-resolution optimization approach (MR-CPI). A series of Monte-Carlo simulations were performed to examine the characteristics of the proposed improvements. Additional tests were done, including different EEG noise levels and variation in electrode-numbers. The results showed highly accurate cortical potential estimations, with a reduction in estimation error by a factor of 3.75 relative to the simple BP-CPI estimation error. We also validated these results with true EEG data. Analyzing these EEGs, we have demonstrated the MR-CPI competence to correctly localize cortical activations in a real environment. The MR-CPI methods were shown to be reliable for estimating cortical potentials, enabling researchers to obtain fast and robust high-resolution EEGs.

Keywords

Multi-scaling Cortical potential imaging Back-projection Optimization Forward solution Monte-Carlo 

Notes

Acknowledgements

This work was supported (in part) by Grant from the MAGNET program of the Israeli OCS and by ElMindA Ltd.

Supplementary material

10548_2018_668_MOESM1_ESM.pdf (2.9 mb)
Supplementary material 1 (PDF 2.90 MB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael
  2. 2.ElMindA, Ltd.HerzliyaIsrael

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