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Sensors 2017, 17(8), 1860; https://doi.org/10.3390/s17081860

Deep Source Localization with Magnetoencephalography Based on Sensor Array Decomposition and Beamforming

1,2,3
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4,5
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1
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
2
Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China
3
Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
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Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
5
Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China
*
Author to whom correspondence should be addressed.
Received: 17 May 2017 / Revised: 31 July 2017 / Accepted: 2 August 2017 / Published: 11 August 2017
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Abstract

In recent years, the source localization technique of magnetoencephalography (MEG) has played a prominent role in cognitive neuroscience and in the diagnosis and treatment of neurological and psychological disorders. However, locating deep brain activities such as in the mesial temporal structures, especially in preoperative evaluation of epilepsy patients, may be more challenging. In this work we have proposed a modified beamforming approach for finding deep sources. First, an iterative spatiotemporal signal decomposition was employed for reconstructing the sensor arrays, which could characterize the intrinsic discriminant features for interpreting sensor signals. Next, a sensor covariance matrix was estimated under the new reconstructed space. Then, a well-known vector beamforming approach, which was a linearly constraint minimum variance (LCMV) approach, was applied to compute the solution for the inverse problem. It can be shown that the proposed source localization approach can give better localization accuracy than two other commonly-used beamforming methods (LCMV, MUSIC) in simulated MEG measurements generated with deep sources. Further, we applied the proposed approach to real MEG data recorded from ten patients with medically-refractory mesial temporal lobe epilepsy (mTLE) for finding epileptogenic zone(s), and there was a good agreement between those findings by the proposed approach and the clinical comprehensive results. View Full-Text
Keywords: magnetoencephalography; deep source localization; iterative matrix decomposition; beamforming; epileptogenic zone; mesial temporal lobe epilepsy magnetoencephalography; deep source localization; iterative matrix decomposition; beamforming; epileptogenic zone; mesial temporal lobe epilepsy
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Hu, Y.; Lin, Y.; Yang, B.; Tang, G.; Liu, T.; Wang, Y.; Zhang, J. Deep Source Localization with Magnetoencephalography Based on Sensor Array Decomposition and Beamforming. Sensors 2017, 17, 1860.

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