This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

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FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

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Convex Analysisの二,三の進展について

The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example.

Two decades of array signal processing research: the parametric approach

The WU-Minn Human Connectome Project: An Overview

An overview of beamforming from a signal-processing perspective is provided, with an emphasis on recent research. Data-independent, statistically optimum, adaptive, and partially adaptive beamforming are discussed. Basic notation, terminology, and concepts are included. Several beamformer implementations are briefly described. >

Beamforming: a versatile approach to spatial filtering

A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites. The weights are chosen to minimize the filter output power subject to a linear constraint. The linear constraint forces the filter to pass brain electrical activity from a specified location, while the power minimization attenuates activity originating at other locations. The estimated output power as a function of location is normalized by the estimated noise power as a function of location to obtain a neural activity index map. Locations of source activity correspond to maxima in the neural activity index map. The method does not require any prior assumptions about the number of active sources of their geometry because it exploits the spatial covariance of the source electrical activity. This paper presents a development and analysis of the method and explores its sensitivity to deviations between actual and assumed data models. The effect on the algorithm of covariance matrix estimation, correlation between sources, and choice of reference is discussed. Simulated and measured data is used to illustrate the efficacy of the approach.

Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

A method of microwave imaging via space-time (MIST) beamforming is proposed for detecting early-stage breast cancer. An array of antennas is located near the surface of the breast and an ultrawideband (UWB) signal is transmitted sequentially from each antenna. The received backscattered signals are passed through a space-time beamformer that is designed to image backscattered signal energy as a function of location. The beamformer spatially focuses the backscattered signals to discriminate against clutter and noise while compensating for frequency-dependent propagation effects. As a consequence of the significant dielectric-properties contrast between normal and malignant tissue, localized regions of large backscatter energy levels in the image correspond to malignant tumors. A data-adaptive algorithm for removing artifacts in the received signals due to backscatter from the skin-breast interface is also presented. The effectiveness of these algorithms is demonstrated using a variety of numerical breast phantoms based on anatomically realistic MRI-derived FDTD models of the breast. Very small (2 mm) malignant tumors embedded within the complex fibroglandular structure of the breast are easily detected above the background clutter. The MIST approach is shown to offer significant improvement in performance over previous UWB microwave breast cancer detection techniques based on simpler focusing schemes.

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Microwave imaging via space-time beamforming for early detection of breast cancer

Ultra-wideband (UWB) microwave imaging has recently been proposed for detecting small malignant breast tumors. In this article, we review the current research status of this approach. First, we introduce the concept of microwave imaging via space-time (MIST) beamforming and related signal-processing algorithms. The objective of these signal-processing techniques is to form a spatial image of scattered microwave energy, and to identify the presence and location of malignant lesions from their scattering signatures. Next, we present numerical studies based on finite-difference time-domain simulations to demonstrate the efficacy of MIST beamforming for detecting small malignant breast lesions in both prone and supine configurations. Finally, the experimental feasibility of UWB microwave imaging is demonstrated using an initial imaging prototype and multilayered breast phantoms.

An overview of ultra-wideband microwave imaging via space-time beamforming for early-stage breast-cancer detection

Microwave imaging via space-time (MIST) beamforming has been proposed recently for detecting small malignant breast tumors. In this paper, we extend the previously presented two-dimensional space-time beamformer design to three-dimensional (3-D), and demonstrate its efficacy using experimental data obtained with a multilayer breast phantom. The breast phantom consists of a homogeneous normal breast tissue simulant covered by a thin layer of skin simulant. A small synthetic malignant tumor is embedded in the breast phantom. We have developed several tumor simulants that yield the range of dielectric contrasts between normal and malignant tissue that are expected in clinical scenarios. A microwave sensor comprised of a synthetic planar array of compact ultrawide-band (UWB) antennas is immersed in a coupling medium above the breast tissue phantom. At each position in the array, the antenna transmits a synthetically generated pulse (1-11 GHz) into the phantom. The received backscatter signals are processed by a data-adaptive algorithm that removes the artifact caused by antenna reverberation and backscatter from the skin-breast interface, followed by 3-D space-time beamforming to image backscattered energy as a function of location. Our investigation includes a numerical (finite difference time domain) and experimental study of the UWB antenna performance in the immersion medium, as well as a study of the influence of malignant-to-normal breast tissue dielectric contrast on dynamic range requirements and tumor detectability. This paper represents the first experimental demonstration of 3-D MIST beamforming in multilayer breast phantoms with malignant-to-normal dielectric contrasts down to 1.5 : 1 for a 4-mm synthetic tumor.

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Microwave imaging via space-time beamforming: experimental investigation of tumor detection in multilayer breast phantoms

Computational electromagnetics models of microwave interactions with the human breast serve as an invaluable tool for exploring the feasibility of new technologies and improving design concepts related to microwave breast cancer detection and treatment. In this paper, we report the development of a collection of anatomically realistic 3-D numerical breast phantoms of varying shape, size, and radiographic density which can readily be used in finite-difference time-domain computational electromagnetics models. The phantoms are derived from T1-weighted MRIs of prone patients. Each MRI is transformed into a uniform grid of dielectric properties using several steps. First, the structure of each phantom is identified by applying image processing techniques to the MRI. Next, the voxel intensities of the MRI are converted to frequency-dependent and tissue-dependent dielectric properties of normal breast tissues via a piecewise-linear map. The dielectric properties of normal breast tissue are taken from the recently completed large-scale experimental study of normal breast tissue dielectric properties conducted by the Universities of Wisconsin and Calgary. The comprehensive collection of numerical phantoms is made available to the scientific community through an online repository.

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Development of Anatomically Realistic Numerical Breast Phantoms With Accurate Dielectric Properties for Modeling Microwave Interactions With the Human Breast

B.D. Van Veen

Papers

Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

Microwave imaging via space-time beamforming for early detection of breast cancer

An overview of ultra-wideband microwave imaging via space-time beamforming for early-stage breast-cancer detection

Microwave imaging via space-time beamforming: experimental investigation of tumor detection in multilayer breast phantoms

Development of Anatomically Realistic Numerical Breast Phantoms With Accurate Dielectric Properties for Modeling Microwave Interactions With the Human Breast