MEG Signal Reconstruction via Low-Rank Matrix Recovery for Source Imaging in Simulations
Source imaging with magnetoencephalography (MEG) has obtained good spatial accuracy on the shallow sources, and has been successfully applied in the brain cognition and the diagnosis of brain disease. However, its utility with locating deep sources may be more challenging. In this study, a new source imaging method was proposed to find real brain activity on deep locations. A sensor array with MEG measurements including 306 channels was represented as a low-rank matrix plus sparse noises. The low-rank matrix was used to reconstruct the MEG signal and remove interference. The source model was estimated using the reconstructed MEG signal and minimum variance beamforming. Simulations with a realistic head model indicated that the proposed method was effective.
KeywordsSource imaging Magnetoencephalography (MEG) Low-rank matrix recovery Beamforming Signal reconstruction
This work was supported by the National Key R&D Program of China (Grant Number: 2016YFF0201002), the National Natural Science Foundation of China (Grant Numbers: 61301005, 61572055), the Beihang University Hefei Innovation Research Institute, Project of ‘The Thousand Talents Plan’ for Young Professionals, and ‘The Thousand Talents Plan’ Workstation between Beihang University and Jiangsu Yuwell Medical Equipment and Supply Co. Ltd.
- 15.Shigeto, H., Morioka, T., Hisada, K., Nishio, S., Ishibashi, H., Kira, D., Tobimatsu, S., Kato, M.: Feasibility and limitations of magnetoencephalographic detection of epileptic discharges: simultaneous recording of magnetic fields and electrocorticography. Neurol. Res. 24, 531–536 (2002)CrossRefGoogle Scholar
- 17.Chen, C.F., Wei, C.P., Wang, Y.C.F.: Low-rank matrix recovery with structural incoherence for robust face recognition. IEEE Conf. Comput. Vis. Pattern Recogn. CVPR, 2618–2625 (2012)Google Scholar
- 25.Wright, J., Ganesh, A., Rao, S., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices. In: Proceedings of the Neural Information Processing Systems (NIPS), pp. 2080–2088 (2009)Google Scholar