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428
Mapping directed influence over the brain using Granger causality and fMRI
- NEUROIMAGE. 25:230--242
, 2005
"... We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguish ..."
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Cited by 120 (4 self)
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We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity.
Multichannel Blind Deconvolution: Fir Matrix Algebra And Separation Of Multipath Mixtures
, 1996
"... A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and mat ..."
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Cited by 90 (0 self)
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A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and matrix algorithms for use in multichannel /multipath problems. Using abstract algebra/group theoretic concepts, information theoretic principles, and the Bussgang property, methods of single channel filtering and source separation of multipath mixtures are merged into a general FIR matrix framework. Techniques developed for equalization may be applied to source separation and vice versa. Potential applications of these results lie in neural networks with feed-forward memory connections, wideband array processing, and in problems with a multi-input, multi-output network having channels between each source and sensor, such as source separation. Particular applications of FIR polynomial matrix alg...
Multivariate Autoregressive Models for Classification of Spontaneous Electroencephalogram During Mental Tasks
- IEEE Transactions on Biomedical Engineering
, 1998
"... This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to c ..."
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Cited by 69 (2 self)
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This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to control a device like a wheelchair. EEG signals from four subjects were recorded while they performed two mental tasks. Quarter-second windows of 6-channel EEG were transformed into four different representations: scalar AR model coefficients, multivariate AR coefficients, eigenvalues of a correlation matrix, and the Karhunen-Loeve transform of the multivariate AR coefficients. Feature vectors defined by these representations were classified with a standard, feedforward neural network trained via the error backpropagation algorithm. The four representations produced similar results, with the multivariate AR coefficients performing slightly better and more consistently with an average classifi...
Exploring estimator bias-variance tradeoffs using the uniform CR bound
- IEEE Trans. on Sig. Proc
, 1996
"... We introduce a plane, which we call the delta-sigma plane, that is indexed by the norm of the estimator bias gradient and the variance of the estimator. The norm of the bias gradient is related to the maximum variation in the estimator bias function over a neighborhood of parameter space. Using a un ..."
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Cited by 57 (17 self)
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We introduce a plane, which we call the delta-sigma plane, that is indexed by the norm of the estimator bias gradient and the variance of the estimator. The norm of the bias gradient is related to the maximum variation in the estimator bias function over a neighborhood of parameter space. Using a uniform Cramer-Rao (CR) bound on estimator variance a delta-sigma tradeoff curve is specied which denes an "unachievable region" of the delta-sigma plane for a specified statistical model. In order to place an estimator on this plane for comparison to the delta-sigma tradeoff curve, the estimator variance, bias gradient, and bias gradient norm must be evaluated. We present a simple and accurate method for experimentally determining the bias gradient norm based on applying a bootstrap estimator to a sample mean constructed from the gradient of the log-likelihood. We demonstrate the methods developed in this paper for linear Gaussian and non-linear Poisson inverse problems.
Classification of EEG signals from four subjects during five mental tasks
- Proceedings of the Conference on Engineering Applications in Neural Networks (EANN’96
, 1996
"... anderson,sijercic¡ ..."
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Spectral Compressive Sensing
, 2010
"... Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals. A great many applications feature smooth or modulated signals that can be modeled as a linear combination of a small number of sinusoids; such signals are sparse in the frequency do ..."
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Cited by 39 (5 self)
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Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals. A great many applications feature smooth or modulated signals that can be modeled as a linear combination of a small number of sinusoids; such signals are sparse in the frequency domain. In practical applications, the standard frequency domain signal representation is the discrete Fourier transform (DFT). Unfortunately, the DFT coefficients of a frequency-sparse signal are themselves sparse only in the contrived case where the sinusoid frequencies are integer multiples of the DFT’s fundamental frequency. As a result, practical DFT-based CS acquisition and recovery of smooth signals does not perform nearly as well as one might expect. In this paper, we develop a new spectral compressive sensing (SCS) theory for general frequency-sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter estimation algorithms from the field of spectrum estimation. Using peridogram and eigen-analysis based spectrum estimates (e.g., MUSIC), our new SCS algorithms significantly outperform the current state-of-the-art CS algorithms while providing provable bounds on the number of measurements required for stable recovery.
Using Signal Processing to Analyze Wireless Data Traffic
- Proc. ACM workshop on Wireless Security
, 2002
"... Experts have long recognized that theoretically it was possible to perform traffic analysis on encrypted packet streams by analyzing the timing of packet arrivals (or transmissions). We report on experiments to realize this possiblity using basic signal processing techniques taken from acoustics to ..."
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Cited by 30 (4 self)
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Experts have long recognized that theoretically it was possible to perform traffic analysis on encrypted packet streams by analyzing the timing of packet arrivals (or transmissions). We report on experiments to realize this possiblity using basic signal processing techniques taken from acoustics to perform traffic analysis on encrypted transmissions over wireless networks. While the work discussed here is preliminary, we are able to demonstrate two very interesting results. First, we can extract timing information, such as round-trip times of TCP connections, from traces of aggregated data traffic. Second, we can determine how data is routed through a network using coherence analysis. These results show that signal processing techniques may prove to be valuable network analysis tools in the future.
Multipath resolving with frequency dependence for wideband channel modeling
- IEEE Trans. Veh. Technol
, 1999
"... Abstract — Multiple ray paths are resolved using high-resolution digital signal processing algorithms. The Cramer–Rao (CR) bound is used as a benchmark where a combination of the singular value decomposition method and the eigen-matrix pencil method is proven to be most successful. The conventional ..."
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Cited by 30 (9 self)
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Abstract — Multiple ray paths are resolved using high-resolution digital signal processing algorithms. The Cramer–Rao (CR) bound is used as a benchmark where a combination of the singular value decomposition method and the eigen-matrix pencil method is proven to be most successful. The conventional complex channel model for wireless propagation is extended to include the frequency-dependent feature of rays which can be used to classify the ray arrivals and provide physical insight of the channel. A novel complex-time model is used to approximate the suggested model. This approach is important to various applications such as equalizer, RAKE receiver, etc., in wireless communication systems. Five key features (noise immunity, robustness, resolution, accuracy, and physical insight) of the proposed algorithm are studied using numerical examples. I.
Frequency Domain Blind MIMO System Identification Based on Second- and Higher Order Statistics
- IEEE TRANS. SIGNAL PROCESSING
, 2001
"... We present a novel frequency-domain framework for the identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent, unobservable inputs. The system frequency response is obtained based on singular value decomposition (SVD) of a matrix constructed based on th ..."
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Cited by 29 (8 self)
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We present a novel frequency-domain framework for the identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent, unobservable inputs. The system frequency response is obtained based on singular value decomposition (SVD) of a matrix constructed based on the power-spectrum and slices of polyspectra of the system output. By appropriately selecting the polyspectra slices, we can create a set of such matrices, each of which could independently yield the solution, or they could all be combined in a joint diagonalization scheme to yield a solution with improved statistical performance. The freedom to select the polyspectra slices allows us to bypass the frequency-dependent permutation ambiguity that is usually associated with frequency domain SVD, while at the same time allows us compute and cancel the phase ambiguity. An asymptotic consistency analysis of the system magnitude response estimate is performed.
