scholarly journals Locating highly correlated sources from MEG with (recursive) (R)DS-MUSIC

2017 ◽  
Author(s):  
Niko Mäkelä ◽  
Matti Stenroos ◽  
Jukka Sarvas ◽  
Risto J. Ilmoniemi
Keyword(s):  
Peak Detection ◽  
Signal Classification ◽  
Double Scanning ◽  
Detection Problem ◽  
Correlated Sources ◽  
Localization Accuracy ◽  
Multiple Signal Classification ◽  
Temporal Correlations ◽  
Highly Correlated ◽  
Signal Sources

AbstractWe introduce a source localization method of the MUltiple Signal Classification (MUSIC) family that can locate brain-signal sources robustly and reliably, irrespective of their temporal correlations. The method, double-scanning (DS) MUSIC, is based on projecting out the topographies of source candidates during topographical scanning in a way that breaks the mutual dependence of highly correlated sources, but keeps the uncorrelated sources intact. We also provide a recursive version of DS-MUSIC (RDS-MUSIC), which overcomes the peak detection problem present in the non-recursive methods. We compare DS-MUSIC and RDS-MUSIC with other localization techniques in numerous simulations with varying source configurations, correlations, and signal-to-noise ratios. DS- and RDS-MUSIC were the most robust localization methods; they had a high success rate and localization accuracy for both uncorrelated and highly correlated sources. In addition, we validated RDS-MUSIC by showing that it successfully locates bilateral synchronous activity from measured auditory-evoked MEG.

ITERATIVE MUSIC FOR HIGHLY CORRELATED EEG/MEG SOURCE LOCALIZATION

2013 ◽  
Vol 25 (02) ◽  
pp. 1350019
Author(s):  
Yuan Cui ◽  
Shan Gao ◽  
Junpeng Zhang
Keyword(s):  
Source Localization ◽  
Real Data ◽  
Signal Classification ◽  
Correlated Sources ◽  
Multiple Signal Classification ◽  
True Source ◽  
Eeg Source Localization ◽  
Auditory Cortices ◽  
Highly Correlated ◽  
Computation Load

This study presented an iterative MUSIC (Multiple Signal Classification) for highly correlated EEG source localization. By suppressing the equivalent false source, the approximate true source location information was obtained. And then, by iteratively suppressing source found in the last iteration, eventually, both of the sources were identified. The method is designed to tackle highly correlated sources, for example, bilateral activations at primary auditory/auditory cortices, at which cases conventional MUSIC has difficulty. Compared with other similar methods, the presented one needs less computation load since it utilizes the minor difference between sources, as can be adequately explained by a theoretical model for correlated sources. Simulation and real data test confirmed its effectiveness.

Study on Resolution Capability of Beam Space MUSIC Estimation Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 2106-2110
Author(s):  
Hao Zhou ◽  
Zhi Jie Huo
Keyword(s):  
Estimation Algorithm ◽  
Signal Classification ◽  
Beam Forming ◽  
Music Algorithm ◽  
Correlated Sources ◽  
Element Space ◽  
Computation Cost ◽  
Multiple Signal Classification ◽  
Resolution Capability ◽  
Sonar Systems

Hydrophone arrays are generally used in modern sonar systems in which beam forming plays an important role. This paper analyzes the beam space MUSIC (multiple signal classification) algorithm for the weakly correlated sources according to changes in the statistical properties of signal and noise, then estimates the azimuth of multi-sources using array element space MUSIC algorithm and beam space MUSIC algorithm accurately, respectively. Finally, problems such as the computation cost of the algorithms, SNR resolution threshold and estimation deviation are discussed based on simulation tests. A conclusion could be drawn that beam space MUSIC algorithm is an effective way to resolve multiple targets in small angle domain.

scholarly journals Combining MUSIC Spatial Sampling and Bootstrap to Estimate Closed Space DOA for Few Samples

2018 ◽  
Vol 3 (3) ◽  
pp. 125-132
Author(s):  
SIDI MOHAMMED HADJ IRID ◽  
SAMIR KAMECHE
Keyword(s):  
Sample Size ◽  
Array Processing ◽  
Doa Estimation ◽  
Classification Algorithm ◽  
Spatial Sampling ◽  
Signal Classification ◽  
Closed Space ◽  
Multiple Signal Classification ◽  
Highly Correlated ◽  
Increase Sample Size

DOA estimation in array processing uses MUSIC (Multiple Signal Classification) algorithm, mainly. It’s the most investigated technique and is very attractive because of its simplicity. However, it meets drawbacks and fails when only very few samples are available and the sources are very close or highly correlated. In these conditions, the problem is more intricate and the detection of targets becomes arduous. To overcome these problems, a new algorithm is developed in this paper. We combine bootstrap technique to increase sample size, spatial sampling and MUSIC method to improve resolution. Through different simulations, the performance and the effectiveness of the proposed approach, referred as spatial Sampling and Bootstrapped technique ‘’SSBoot’’, are demonstrated.

scholarly journals Bearing and Range Estimation Algorithm for Buried Object in Underwater Acoustics

2009 ◽  
Vol 2009 ◽  
pp. 1-4
Author(s):  
Dong Han ◽  
Caroline Fossati ◽  
Salah Bourennane ◽  
Zineb Saidi
Keyword(s):  
Underwater Acoustics ◽  
Classical Model ◽  
Acoustic Scattering ◽  
Estimation Algorithm ◽  
Signal Classification ◽  
Music Algorithm ◽  
Range Estimation ◽  
Multiple Signal Classification ◽  
Buried Objects ◽  
Buried Object

A new algorithm which associates (Multiple Signal Classification) MUSIC with acoustic scattering model for bearing and range estimation is proposed. This algorithm takes into account the reflection and the refraction of wave in the interface of water-sediment in underwater acoustics. A new directional vector, which contains the Direction-Of-Arrival (DOA) of objects and objects-sensors distances, is used in MUSIC algorithm instead of classical model. The influence of the depth of buried objects is discussed. Finally, the numerical results are given in the case of buried cylindrical shells.

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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