Biomedical subjects

Barry D Van Veen

Publications and source records attributed to Barry D Van Veen.

6 recordsLinked to original sources

PubMed2006

MEG and EEG source localization in beamspace.

Beamspace methods are applied to EEG/MEG source localization problems in this paper. Beamspace processing involves passing the data through a linear transformation that reduces the data dimension prior to applying a desired statistical signal processing algorithm. This process generally reduces the data requirements of the subsequent algorithm. We present one approach for designing beamspace transformations that are optimized to preserve source activity located within a given region of interest and show that substantial reductions in dimension are obtained with negligible signal loss. Beamspace versions of maximum likelihood dipole fitting, MUSIC, and minimum variance beamforming source localization algorithms are presented. The performance improvement offered by the beamspace approach with limited data is demonstrated by bootstrapping somatosensory data to evaluate the variability of the source location estimates obtained with each algorithm. The quantitative benefits of beamspace processing depend on the algorithm, signal to noise ratio, and amount of data. Dramatic performance improvements are obtained in scenarios with low signal to noise ratio and a small number of independent data samples.

Brain↗

PubMed2006

Linear minimum mean-square error filtering for evoked responses: application to fetal MEG.

This paper describes a linear minimum mean-squared error (LMMSE) approach for designing spatial filters that improve the signal-to-noise ratio (SNR) of multiepoch evoked response data. This approach does not rely on availability of a forward solution and thus is applicable to problems in which a forward solution is not readily available, such as fetal magnetoencephalography (fMEG). The LMMSE criterion leads to a spatial filter that is a function of the autocorrelation matrix of the data and the autocorrelation matrix of the signal. The signal statistics are unknown, so we approximate the signal autocorrelation matrix using the average of the data across epochs. This approximation is reasonable provided the mean of the noise is zero across epochs and the signal mean is significant. An analysis of the error incurred using this approximation is presented. Calculations of SNR for the exact and approximate LMMSE filters and simple averaging for the rank-1 signal case are shown. The effectiveness of the method is demonstrated with simulated evoked response data and fetal MEG data.

Algorithms↗

PubMed2006

Cortical patch basis model for spatially extended neural activity.

A new source model for representing spatially distributed neural activity is presented. The signal of interest is modeled as originating from a patch of cortex and is represented using a set of basis functions. Each cortical patch has its own set of bases, which allows representation of arbitrary source activity within the patch. This is in contrast to previously proposed cortical patch models which assume a specific distribution of activity within the patch. We present a procedure for designing bases that minimize the normalized mean squared representation error, averaged over different activity distributions within the patch. Extension of existing algorithms to the basis function framework is straightforward and is illustrated using linearly constrained minimum variance (LCMV) spatial filtering and maximum-likelihood signal estimation/generalized likelihood ratio test (ML/GLRT). The number of bases chosen for each patch determines a tradeoff between representation accuracy and the ability to differentiate between distinct patches. We propose choosing the minimum number of bases that satisfy a constraint on the normalized mean squared representation accuracy. A mismatch analysis for LCMV and ML/GLRT is presented to show that this is an appropriate strategy for choosing the number of bases. The effectiveness of the patch basis model is demonstrated using real and simulated evoked response data. We show that significant changes in performance occur as the number of basis functions varies, and that very good results are obtained by allowing modest representation error.

Algorithms↗

PubMed2005

Ultrawideband microwave breast cancer detection: a detection-theoretic approach using the generalized likelihood ratio test.

Microwave imaging has been suggested as a promising modality for early-stage breast cancer detection. In this paper, we propose a statistical microwave imaging technique wherein a set of generalized likelihood ratio tests (GLRT) is applied to microwave backscatter data to determine the presence and location of strong scatterers such as malignant tumors in the breast. The GLRT is formulated assuming that the backscatter data is Gaussian distributed with known covariance matrix. We describe the method for estimating this covariance matrix offline and formulating a GLRT for several heterogeneous two-dimensional (2-D) numerical breast phantoms, several three-dimensional (3-D) experimental breast phantoms, and a 3-D numerical breast phantom with a realistic half-ellipsoid shape. Using the GLRT with the estimated covariance matrix and a threshold chosen to constrain the false discovery rate (FDR) of the image, we show the capability to detect and localize small (<0.6 cm) tumors in our numerical and experimental breast phantoms even when the dielectric contrast of the malignant-to-normal tissue is below 2:1.

Algorithms↗

PubMed2004

Maximum-likelihood estimation of low-rank signals for multiepoch MEG/EEG analysis.

A maximum-likelihood-based algorithm is presented for reducing the effects of spatially colored noise in evoked response magneto- and electro-encephalography data. The repeated component of the data, or signal of interest, is modeled as the mean, while the noise is modeled as the Kronecker product of a spatial and a temporal covariance matrix. The temporal covariance matrix is assumed known or estimated prior to the application of the algorithm. The spatial covariance structure is estimated as part of the maximum-likelihood procedure. The mean matrix representing the signal of interest is assumed to be low-rank due to the temporal and spatial structure of the data. The maximum-likelihood estimates of the components of the low-rank signal structure are derived in order to estimate the signal component. The relationship between this approach and principal component analysis (PCA) is explored. In contrast to prestimulus-based whitening followed by PCA, the maximum-likelihood approach does not require signal-free data for noise whitening. Consequently, the maximum-likelihood approach is much more effective with nonstationary noise and produces better quality whitening for a given data record length. The efficacy of this approach is demonstrated using simulated and real MEG data.

Algorithms↗

PubMed2003

Statistical performance analysis of signal variance-based dipole models for MEG/EEG source localization and detection.

A set of dipole fitting algorithms that incorporate different assumptions about the variability of the signal component into their mathematical models is presented and analyzed. Dipole fitting is performed by minimizing the squared error between the selected data model and available data. Dipole models based on moments that have 1) constant amplitude and orientation, 2) variable amplitude and fixed known orientation, 3) variable amplitude and fixed unknown orientation, and 4) variable amplitude and variable orientation are considered. The presence of a dipolar source is determined by comparing the fractional energy explained by the dipole model to a threshold. Source localization is accomplished by searching to find the location that explains the largest fractional signal energy using a dipole model. Expressions for the probability of a false positive decision and probability of correct detection are derived and used to evaluate the effect of variability in the dipole on performance and to address the effects of model mismatch and location errors. Simulated and measured data experiments are presented to illustrate the performance of both detection and localization methods. The results indicate that models which account for variance outperform the constant orientation and magnitude model even when the number of observations is relatively small and the signal of interest contains a very modest variance component.

Action Potentials↗