Solution for MEG inverse problem using Signal Space Separation and Magnetic Field Tomography

2011 ◽  
Author(s):  
Tadashi Kitahara ◽  
Satoshi Honda ◽  
Tuan D. Pham ◽  
Xiaobo Zhou ◽  
Hiroshi Tanaka ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Inverse Problem ◽  
Signal Space ◽  
Signal Space Separation ◽  
Meg Inverse Problem ◽  
Magnetic Field Tomography ◽  
Space Separation

scholarly journals Signal Space Separation Method for a Biomagnetic Sensor Array Arranged on a Flat Plane for Magnetocardiographic Applications: A Computer Simulation Study

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Kensuke Sekihara
Keyword(s):  
Magnetic Field ◽  
Sensor Array ◽  
Sensor Arrays ◽  
Signal Space ◽  
Signal Space Separation ◽  
Vector Sensor ◽  
Vector Sensors ◽  
Modified Sensor ◽  
Space Separation ◽  
The Magnetic Field

Although the signal space separation (SSS) method can successfully suppress interference/artifacts overlapped onto magnetoencephalography (MEG) signals, the method is considered inapplicable to data from nonhelmet-type sensor arrays, such as the flat sensor arrays typically used in magnetocardiographic (MCG) applications. This paper shows that the SSS method is still effective for data measured from a (nonhelmet-type) array of sensors arranged on a flat plane. By using computer simulations, it is shown that the optimum location of the origin can be determined by assessing the dependence of signal and noise gains of the SSS extractor on the origin location. The optimum values of the parameters LC and LD, which, respectively, indicate the truncation values of the multipole-order ℓ of the internal and external subspaces, are also determined by evaluating dependences of the signal, noise, and interference gains (i.e., the shield factor) on these parameters. The shield factor exceeds 104 for interferences originating from fairly distant sources. However, the shield factor drops to approximately 100 when calibration errors of 0.1% exist and to 30 when calibration errors of 1% exist. The shielding capability can be significantly improved using vector sensors, which measure the x, y, and z components of the magnetic field. With 1% calibration errors, a vector sensor array still maintains a shield factor of approximately 500. It is found that the SSS application to data from flat sensor arrays causes a distortion in the signal magnetic field, but it is shown that the distortion can be corrected by using an SSS-modified sensor lead field in the voxel space analysis.

scholarly journals Utility of temporally-extended signal space separation algorithm for magnetic noise from vagal nerve stimulators

2013 ◽  
Vol 124 (7) ◽  
pp. 1277-1282 ◽  
Author(s):  
Yosuke Kakisaka ◽  
John C. Mosher ◽  
Zhong I. Wang ◽  
Kazutaka Jin ◽  
Anne-Sophie Dubarry ◽  
...  
Keyword(s):  
Vagal Nerve ◽  
Magnetic Noise ◽  
Separation Algorithm ◽  
Signal Space ◽  
Signal Space Separation ◽  
Space Separation

Calibration of a multichannel MEG system based on the Signal Space Separation method

2012 ◽  
Vol 57 (15) ◽  
pp. 4855-4870 ◽  
Author(s):  
F Chella ◽  
F Zappasodi ◽  
L Marzetti ◽  
S Della Penna ◽  
V Pizzella
Keyword(s):  
Separation Method ◽  
Signal Space ◽  
Signal Space Separation ◽  
Space Separation
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