scholarly journals A comparison of minimum norm and MUSIC for a combined MEG/EEG sensor array

2012 ◽  
Vol 10 ◽  
pp. 99-104 ◽  
Author(s):  
H. Ahrens ◽  
F. Argin ◽  
L. Klinkenbusch
Keyword(s):  
Neuronal Activity ◽  
Sensor Array ◽  
Focal Epilepsy ◽  
Epileptic Seizures ◽  
Signal Classification ◽  
Medical Applications ◽  
Minimum Norm ◽  
Multiple Signal Classification ◽  
Source Localisation ◽  
Multiple Signal

Abstract. Many different algorithms for imaging neuronal activity with magnetoencephalography (MEG) or electroencephalography (EEG) have been developed so far. We validate the result of other authors that a combined MEG/EEG sensor array provides smaller source localisation errors than a single MEG or single EEG sensor array for the same total number of sensors. We show that Multiple Signal Classification (MUSIC) provides smaller localisation errors than an unweighted minimum norm method for activity located in the cortical sulcus regions. This is important for many medical applications, e.g. the localisation of the origin of epileptic seizures (focal epilepsy) that can be located very deep in the cortical sulcus.

scholarly journals Multiple Signal Classification-Based Impact Localization in Composite Structures Using Optimized Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 8 (9) ◽  
pp. 1447 ◽  
Author(s):  
Yongteng Zhong ◽  
Jiawei Xiang ◽  
Xiaoyu Chen ◽  
Yongying Jiang ◽  
Jihong Pang
Keyword(s):  
Composite Structures ◽  
Sensor Array ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Classification ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
Mode Decomposition ◽  
The Impact ◽  
Impact Localization

Multiple signal classification (MUSIC) algorithm-based structural health monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, in previous MUSIC-based impact location methods, the narrowband signals at a particular central frequency had to be extracted from the wideband Lamb waves induced by each impact using a wavelet transform. Additionally, the specific center frequency had to be obtained after carefully analyzing the impact signal, which is time consuming. Aiming at solving this problem, this paper presents an improved approach that combines the optimized ensemble empirical mode decomposition (EEMD) and two-dimensional multiple signal classification (2D-MUSIC) algorithm for real-time impact localization on composite structures. Firstly, the impact signal at an unknown position is obtained using a unified linear sensor array. Secondly, the fast Hilbert Huang transform (HHT) with an optimized EEMD algorithm is introduced to extract intrinsic mode functions (IMFs) from impact signals. Then, all IMFs in the whole frequency domain are directly used as the input vector of the 2D-MUSIC model separately to locate the impact source. Experimental data collected from a cross-ply glass fiber reinforced composite plate are used to validate the proposed approach. The results show that the use of optimized EEMD and 2D-MUSIC is suitable for real-time impact localization of composite structures.

Azimuthal multiple signal classification of dispersive and aliased surface waves recorded in 3D seismic acquisition

Geophysics ◽  
2021 ◽  
pp. 1-84
Author(s):  
Chunying Yang ◽  
Wenchuang Wang
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Body Waves ◽  
Signal Classification ◽  
Dispersion Pattern ◽  
Velocity Spectrum ◽  
Low Velocity ◽  
Surface Wave Dispersion ◽  
Multiple Signal Classification ◽  
Multiple Signal

Irregular acquisition geometry causes discontinuities in the appearance of surface wave events, and a large offset causes seismic records to appear as aliased surface waves. The conventional method of sampling data affects the accuracy of the dispersion spectrum and reduces the resolution of surface waves. At the same time, ”mode kissing” of the low-velocity layer and inhomogeneous scatterers requires a high-resolution method for calculating surface wave dispersion. This study tested the use of the multiple signal classification (MUSIC) algorithm in 3D multichannel and aliased wavefield separation. Azimuthal MUSIC is a useful method to estimate the phase velocity spectrum of aliased surface wave data, and it represent the dispersion spectra of low-velocity and inhomogeneous models. The results of this study demonstrate that mode-kissing affects dispersion imaging, and inhomogeneous scatterers change the direction of surface-wave propagation. Surface waves generated from the new propagation directions are also dispersive. The scattered surface wave has a new dispersion pattern different to that of the entire record. Diagonal loading was introduced to improve the robustness of azimuthal MUSIC, and numerical experiments demonstrate the resultant effectiveness of imaging aliasing surface waves. A phase-matched filter was applied to the results of azimuthal MUSIC, and phase iterations were unwrapped in a fast and stable manner. Aliased surface waves and body waves were separated during this process. Overall, field data demonstrate that azimuthal MUSIC and phase-matched filters can successfully separate aliased surface waves.

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