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.

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 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

scholarly journals Frequency Invariant Beamforming for a Small-Sized Bi-Cone Acoustic Vector–Sensor Array

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 661
Author(s):  
Erzheng Fang ◽  
Chenyang Gui ◽  
Desen Yang ◽  
Zhongrui Zhu
Keyword(s):  
Sensor Array ◽  
Three Dimensional ◽  
Sensor Arrays ◽  
Signal Frequency ◽  
Acoustic Vector Sensor ◽  
Mechanical Coupling ◽  
Vector Sensor ◽  
Coupling System ◽  
Simulation Results ◽  
Vector Sensor Array

In this work, we design a small-sized bi-cone acoustic vector-sensor array (BCAVSA) and propose a frequency invariant beamforming method for the BCAVSA, inspired by the Ormia ochracea’s coupling ears and harmonic nesting. First, we design a BCAVSA using several sets of cylindrical acoustic vector-sensor arrays (AVSAs), which are used as a guide to construct the constant beamwidth beamformer. Due to the mechanical coupling system of the Ormia ochracea’s two ears, the phase and amplitude differences of acoustic signals at the bilateral tympanal membranes are magnified. To obtain a virtual BCAVSA with larger interelement distances, we then extend the coupling magnified system into the BCAVSA by deriving the expression of the coupling magnified matrix for the BCAVSA and providing the selecting method of coupled parameters for fitting the underwater signal frequency. Finally, the frequency invariant beamforming method is developed to acquire the constant beamwidth pattern in the three-dimensional plane by deriving several sets of the frequency weighted coefficients for the different cylindrical AVSAs. Simulation results show that this method achieves a narrower mainlobe width compared to the original BCAVSA. This method has lower sidelobes and a narrower mainlobe width compared to the coupling magnified bi-cone pressure sensor array.

scholarly journals Indoor Positioning Using Magnetic Fingerprint Map Captured by Magnetic Sensor Array

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5707
Author(s):  
Ching-Han Chen ◽  
Pi-Wei Chen ◽  
Pi-Jhong Chen ◽  
Tzung-Hsin Liu
Keyword(s):  
Magnetic Field ◽  
Sensor Array ◽  
Probabilistic Neural Network ◽  
Indoor Positioning ◽  
Magnetic Sensor ◽  
Magnetic Sensors ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
The Magnetic Field

By collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user’s location. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to “local temporal-spatial variation”, it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning. This research proposes a new magnetic indoor positioning method, which combines a magnetic sensor array composed of three magnetic sensors and a recurrent probabilistic neural network (RPNN) to realize a high-precision indoor positioning system. The magnetic sensor array can detect subtle magnetic anomalies and spatial variations to improve the stability and accuracy of magnetic field fingerprint maps, and the RPNN model is built for recognizing magnetic field fingerprint. We implement an embedded magnetic sensor array positioning system, which is evaluated in an experimental environment. Our method can reduce the noise caused by the spatial-temporal variation of the magnetic field, thus greatly improving the indoor positioning accuracy, reaching an average positioning accuracy of 0.78 m.

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