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.

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.

Sound Source Localization Based on Microphone Uniform Linear Array

2013 ◽  
Vol 748 ◽  
pp. 634-639 ◽  
Author(s):  
Jian Rong Wang ◽  
Ju Zhang ◽  
Song Gun Hyon ◽  
Jian Guo Wei
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Linear Array ◽  
Weighted Average ◽  
Classification Algorithm ◽  
Signal Classification ◽  
Uniform Linear Array ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
The Impact

In order to locate the sound source based on the microphone uniform linear array and reduce the impact of noise and reflection, improved multiple signal classification algorithm and a kind of weighted average filters be used in this paper. According to the microphone array speech processing characteristics, we improved the traditional multiple signal classification algorithm and designed a kind of weighted average filters. Then, we made the computer simulation experiments. The experimental results show that the location of the sound source is the peak with the highest power in the spatial spectrum. Besides, the frequency domain diagram is more smoothly and the power of the noise and reflection is effectively reduced except sound source through the weighted average processing. Therefore, improved multiple signal classification algorithm can achieve sound source localization based on the microphone uniform linear array. And the impact of the noise and reflection is effectively reduced by processing of the weighted average filters.

Impact source localization in plate utilizing multiple signal classification

2012 ◽  
Vol 227 (4) ◽  
pp. 703-713 ◽  
Author(s):  
HongJun Yang ◽  
Young Jun Lee ◽  
Sang Kwon Lee
Keyword(s):  
Time Delay ◽  
Group Velocity ◽  
Source Localization ◽  
Direction Of Arrival ◽  
Signal Classification ◽  
Classification Method ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
The Impact ◽  
Time Delay Of Arrival

This article proposed a multiple signal classification method based on array signal processing for impact source localization in a plate. For source localization, the direction of arrival of the wave caused by an impact on a plate and the distance between the impact position and sensor should be estimated. The direction of arrival can be estimated accurately using the multiple signal classification method; the distance can be obtained using the time delay of arrival and the group velocity of the Lamb wave in a plate. The time delay of arrival is experimentally estimated using the continuous wavelet transform for the wave. The group velocity is theoretically obtained based on the elastodynamic theory.

scholarly journals Mixed-Field Source Localization Based on the Non-Hermitian Matrix

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 56
Author(s):  
Minggang Mo ◽  
Zhaowei Sun
Keyword(s):  
Source Localization ◽  
Hermitian Matrix ◽  
High Order ◽  
Signal Classification ◽  
Range Estimation ◽  
Field Source ◽  
Multiple Signal Classification ◽  
Schmidt Orthogonalization ◽  
Simulation Results ◽  
Mixed Field

In this paper, an efficient high-order multiple signal classification (MUSIC)-like method is proposed for mixed-field source localization. Firstly, a non-Hermitian matrix is designed based on a high-order cumulant. One of the steering matrices, that is related only with the directions of arrival (DOA), is proved to be orthogonal with the eigenvectors corresponding to the zero eigenvalues. The other steering matrix that contains the information of both the DOA and range is proved to span the same column subspace with the eigenvectors corresponding to the non-zero eigenvalues. By applying the Gram–Schmidt orthogonalization, the range estimation can be achieved one by one after substituting each estimated DOA. The analysis shows that the computational complexity of the proposed method is lower than other methods, and the effectiveness of the proposed method is shown with some simulation results.

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.

Sign in / Sign up

Export Citation Format

Share Document