Efficient localization of synchronous EEG source activities using a modified RAP-MUSIC algorithm

Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Spokane, WA, USA
IEEE Transactions on Biomedical Engineering (Impact Factor: 2.35). 05/2006; 53(4):652 - 661. DOI: 10.1109/TBME.2006.870236
Source: IEEE Xplore


Synchronization across different brain regions is suggested to be a possible mechanism for functional integration. Noninvasive analysis of the synchronization among cortical areas is possible if the electrical sources can be estimated by solving the electroencephalography inverse problem. Among various inverse algorithms, spatio-temporal dipole fitting methods such as RAP-MUSIC and R-MUSIC have demonstrated superior ability in the localization of a restricted number of independent sources, and also have the ability to reliably reproduce temporal waveforms. However, these algorithms experience difficulty in reconstructing multiple correlated sources. Accurate reconstruction of correlated brain activities is critical in synchronization analysis. In this study, we modified the well-known inverse algorithm RAP-MUSIC to a multistage process which analyzes the correlation of candidate sources and searches for independent topographies (ITs) among precorrelated groups. Comparative studies were carried out on both simulated data and clinical seizure data. The results demonstrated superior performance with the modified algorithm compared to the original RAP-MUSIC in recovering synchronous sources and localizing the epileptiform activity. The modified RAP-MUSIC algorithm, thus, has potential in neurological applications involving significant synchronous brain activities.

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Available from: Paul Schimpf, Oct 16, 2012
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    • "ple signal classification (MUSIC) [1]–[4], FINE [5]–[7], minimum variance beamforming (MVB) [8], vector-type Borgiotti– Kaplan beamforming [9], weighted minimum norm [10], standardized low-resolution electromagnetic tomography [11]–[13], and dipole fitting [14]–[16]. "
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    ABSTRACT: In the electroencephalogram (EEG) or magneto-encephalogram (MEG) context, brain source localization methods that rely on estimating second order statistics often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. Due to the correlated background EEG/MEG signal, an adaptive approach to localization is desirable. Previous work has addressed these issues by reducing the adaptive degrees of freedom (DoFs). This reduction results in decreased resolution and accuracy of the estimated source configuration. This paper develops and tests a new multistage adaptive processing technique based on the minimum variance beamformer for brain source localization that has been previously used in the radar statistical signal processing context. This processing, referred to as the fast fully adaptive (FFA) approach, can significantly reduce the required sample support, while still preserving all available DoFs. To demonstrate the performance of the FFA approach in the limited data scenario, simulation and experimental results are compared with two previous beamforming approaches; i.e., the fully adaptive minimum variance beamforming (MVB) method and the Beamspace beamforming method. Both simulation and experimental results demonstrate that the FFA method can localize all types of brain activity more accurately than the other approaches with limited data.
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    • "The multiple signal classification (MUSIC) algorithm [3] belongs to this category. Unfortunately, most techniques have difficulties in separating highly correlated sources and can, thus, produce large localization errors when such sources are present [4]. "
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    ABSTRACT: Epilepsy is a neurological disorder characterized by seizures, i.e. abnormal synchronous activity of neurons in the brain. During a focal seizure the epileptic activity spreads rapidly from the ictal onset region to neighboring brain areas. ElectroEncephaloGraphy (EEG) is a commonly used technique to diagnose epilepsy. EEG has a high temporal resolution which allows us to investigate the dynamics of the underlying brain activity. Due to the rapid propagation of a seizure, the seizure can originate from a network of brain regions which are simultaneously active before being noticeable on the EEG. In this paper we investigate two state of the art source localization techniques, the Recursive Applied and Projected (RAP) and the pre-correlated and orthogonally projected (POP) multiple signal classification (MUSIC) algorithm, to identify the location of the driver behind the simulated epileptic brain network. Furthermore we investigate the applicability of connectivity analysis to identify the source driving the underlying brain network. We showed that the POP-MUSIC algorithm outperforms the RAP-MUSIC algorithm to identify the locations of the simultaneous brain activity. Furthermore, we showed the feasibility of identifying the driver behind a brain network by POP-MUSIC algorithm followed by connectivity analysis.
    No preview · Conference Paper · Jun 2011
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    • "It performs a grid search employing the noise subspace of the data utilizing the singular value decomposition (SVD) of the EEG data. Regarding correlated sources, in [4], a modified version of MUSIC is developed to deal with correlated sources. In [3], the authors incorporated the real geometry of the head in the calculation of the dipole parameters. "
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    ABSTRACT: A novel algorithm for the localization of event-related potential (ERP) sources within the brain is proposed here. In this technique, spatial notch filters are developed to exploit the multichannel electroencephalogram data together with a model of ERP with variable parameters in order to accurately localize the corresponding ERP signal sources. The algorithm is robust in the presence of reasonably high noise. The performance of the proposed system has been compared to that of linear constrained minimum variance (LCMV) beamformer for different noise and correlation levels and its superiority has been demonstrated.
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