evaluation of eeg localization methods using realistic simulations of interictal spikes performing an accurate localization of sources of interictal spikes from eeg scalp measurements is of particular interest during the presurgical investigation of epilepsy the purpose of this paper is to study the ability of six distributed source localization methods to recover extended sources of activated cortex due to the frequent lack of a gold standard to evaluate source localization methods our evaluation was performed in a controlled environment using realistic simulations of eeg interictal spikes involving several anatomical locations with several spatial extents simulated data were corrupted by physiological eeg noise simulations involving pairs of sources with the same amplitude were also studied in addition to standard validation criteria e g geodesic distance or mean square error we proposed an original criterion dedicated to assess detection accuracy based on receiver operating characteristic roc analysis six source localization methods were evaluated the minimum norm the minimum norm weighted by multivariate source prelocalization msp cortical loreta with or without additional minimum norm regularization and two derivations of the maximum entropy on the mean mem approach results showed that loreta based and mem based methods were able to accurately recover sources of different spatial extents with the exception of sources in temporo mesial and fronto mesial regions several spurious sources were generated by those methods however whereas methods using the msp always located very accurately the maximum of activity but not its spatial extent these findings suggest that one should always take into account the results from different localization methods when analyzing real interictal spikes

eeg extended source localization tensor based vs conventional methods the localization of brain sources based on eeg measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed however their performance is limited in the case of several simultaneously active brain regions and low signal to noise ratios to overcome these problems tensor based preprocessing can be applied which consists in constructing a space time frequency stf or space time wave vector stwv tensor and decomposing it using the canonical polyadic cp decomposition in this paper we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition furthermore we conduct a detailed study of the tensor based preprocessing methods including an analysis of their theoretical foundation their computational complexity and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sloreta cortical loreta cloreta and 4 exso music our objective consists on the one hand in demonstrating the gain in performance that can be achieved by tensor based preprocessing and on the other hand in pointing out the limits and drawbacks of this method finally we validate the stf and stwv techniques on real measurements to demonstrate their usefulness for practical applications

variational geometric and statistical methods for modeling brain anatomy and function we survey the recent activities of the odyssee laboratory in the area of the application of mathematics to the design of models for studying brain anatomy and function we start with the problem of reconstructing sources in meg and eeg and discuss the variational approach we have developed for solving these inverse problems this motivates the need for geometric models of the head we present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical magnetic resonance mr images anatomical connectivity can be extracted from diffusion tensor magnetic resonance images but in the current state of the technology it must be preceded by a robust estimation and regularization stage we discuss our work based on variational principles and show how the results can be used to track fibers in the white matter wm as geodesics in some riemannian space we then go to the statistical modeling of functional magnetic resonance imaging fmri signals from the viewpoint of their decomposition in a pseudo deterministic and stochastic part that we then use to perform clustering of voxels in a way that is inspired by the theory of support vector machines and in a way that is grounded in information theory multimodal image matching is discussed next in the framework of image statistics and partial differential equations pdes with an eye on registering fmri to the anatomy the paper ends with a discussion of a new theory of random shapes that may prove useful in building anatomical and functional atlases

cortical surface based analysis ii inflation flattening and a surface based coordinate system the surface of the human cerebral cortex is a highly folded sheet with the majority of its surface area buried within folds as such it is a difficult domain for computational as well as visualization purposes we have therefore designed a set of procedures for modifying the representation of the cortical surface to i inflate it so that activity buried inside sulci may be visualized ii cut and flatten an entire hemisphere and iii transform a hemisphere into a simple parameterizable surface such as a sphere for the purpose of establishing a surface based coordinate system r 1999 academic press

a distributed spatio temporal eeg meg inverse solver we propose a novel l1l2 norm inverse solver for estimating the sources of eeg meg signals based on the standard l1 norm inverse solvers this sparse distributed inverse solver integrates the l1 norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and spiky reconstructed signals often produced by the currently used sparse solvers the joint spatio temporal model leads to a cost function with an l1l2 norm regularizer whose minimization can be reduced to a convex second order cone programming socp problem and efficiently solved using the interior point method the efficient computation of the socp problem allows us to implement permutation tests for estimating statistical significance of the inverse solution validation with simulated and human meg data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting but without the need to specify the number of dipole sources in advance furthermore the l1l2 norm solver achieves fewer false positives and a better representation of the source locations than the conventional l2 minimum norm estimates

spatial spectrum estimation in a location sector multiple narrowband source localization using arbitrarily configured arrays and spatial spectrum estimation is considered a new eigenspace based approach which uses projections onto a particular vector or vector set in the estimated noise only subspace is described several closest vector estimators are developed by using different measures of closeness first closest is a novel full dimensional element space approach to spatial spectrum estimation which has important performance advantages relative to pertinent established spatial spectrum estimators it incorporates a priori knowledge of the array manifold over a location sector of interest to provide snr spectral resolution thresholds which are lower than those of min norm for some arrays substantially lower second relationships between the closest approach and several established approaches to spatial spectrum estimation are established for a linear equispaced array min norm is shown to be a special case of the closest approach one which is based on projection onto a noise only subspace vector which is close to the array manifold over the entire field of view

multiple emitter location and signal parameter estimation processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions the general problem considers sensors with arbitrary locations and arbitrary directional characteristics gain phase polarization in a noise interference environment of arbitrary covariance matrix this report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution a description is given of the multiple signal classification music algorithm which provides asymptotically unbiased estimates of 1 number of incident wavefronts present 2 directions of arrival doa or emitter locations 3 strengths and cross correlations among the incident waveforms 4 noise interference strength examples and comparisons with methods based on maximum likelihood ml and maximum entropy me as well as conventional beamforming are included an example of its use as a multiple frequency estimator operating on time series is included

two numerical methods for recovering small inclusions from the scattering amplitude at a fixed frequency in this paper two noniterative algorithms for locating small electromagnetic inclusions from the scattering amplitude at a fixed frequency are developed in particular a variety of numerical results is presented to highlight their potential and their limitations

esprit estimation of signal parameters via rotational invariance techniques an approach to the general problem of signal parameter estimation is described the algorithm differs from its predecessor in that a total least squares rather than a standard least squares criterion is used although discussed in the context of direction of arrival estimation esprit can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise it exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure the technique when applicable manifests significant performance and computational advantages over previous algorithms such as mem capon s mlm and music

bit error probabilities mdpsk over the nonselective rayleigh fading channel with diversity reception data transmission using m ary differential phase shift keying mdpsk over the nonselective rayleigh fading channel with diversity reception is considered while previous studies on error probability mostly assume no fading fluctuation the author considers exclusively the case in which the fading process fluctuates from one symbol interval to the next exact bit error probability results for 2 4 and 8 dpsk as well as tight upper bounds are derived some applications of the results are discussed

conditional correlation as a measure of mediated interactivity in fmri and meg eeg many measures have been proposed so far to extract brain functional interactivity from functional magnetic resonance imaging fmri and magnetoencephalography electroencephalography meg eeg data sets unfortunately none has been able to provide a relevant self contained and common definition of brain interaction in this paper we propose a first step in this direction we first introduce a common terminology together with a cross modal definition of interaction in this setting we investigate the commonalities shared by some measures of interaction proposed in the literature we show that temporal correlation nonlinear correlation mutual information generalized synchronization phase synchronization coherence and phase locking value plv actually measure the same quantity namely correlation when one is investigating linear interactions between independently and identically distributed gaussian variables we also demonstrate that these data driven measures can only partly account for the interaction patterns that can be expressed by the effective connectivity of structural equation modeling sem to bridge this gap we suggest the use of conditional correlation which is shown to be related to mediated interaction

reconstruction of a small inclusion in a two dimensional open waveguide we consider wave propagation in a perturbed open waveguide we provide a new asymptotic expansion for the scattered wave when the inclusion is of small diameter we design a music multiple signal classification type of algorithm for locating the inclusion and illustrate its viability in numerical examples

music algorithm for location searching of dielectric anomalies from s parameters using microwave imaging abstract motivated by the biomedical engineering used in early stage breast cancer detection we investigated the use of multiple signal classification music algorithm for location searching of small anomalies using s parameters we considered the application of music to functional imaging where a small number of dipole antennas are used our approach is based on the application of born approximation or physical factorization we analyzed cases in which the anomaly is respectively small and large in relation to the wavelength and the structure of the left singular vectors is linked to the nonzero singular values of a multi static response msr matrix whose elements are the s parameters using simulations we demonstrated the strengths and weaknesses of the music algorithm in detecting both small and extended anomalies

nonlinear denoising of transient signals with application to event related potentials we present a new wavelet based method for the denoising of event related potentials erps employing techniques recently developed for the paradigm of deterministic chaotic systems the denoising scheme has been constructed to be appropriate for short and transient time sequences using circular state space embedding its effectiveness was successfully tested on simulated signals as well as on erps recorded from within a human brain the method enables the study of individual erps against strong ongoing brain electrical activity

truncated rap music trap music for meg and eeg source localization electrically active brain regions can be located applying multiple signal classification music on magneto or electroencephalographic meg eeg data we introduce a new music method called truncated recursively applied and projected music trap music it corrects a hidden deficiency of the conventional rap music algorithm which prevents estimation of the true number of brain signal sources accurately the correction is done by applying a sequential dimension reduction to the signal subspace projection we show that trap music significantly improves the performance of music type localization in particular it successfully and robustly locates active brain regions and estimates their number we compare trap music and rap music in simulations with varying key parameters e g signal to noise ratio correlation between source time courses and initial estimate for the dimension of the signal space in addition we validate trap music with measured meg data we suggest that with the proposed trap music method music type localization could become more reliable and suitable for various online and offline meg and eeg applications

image reconstruction and restoration overview of common estimation structures and problems developments in the theory of image reconstruction and restoration over the past 20 or 30 years are outlined particular attention is paid to common estimation structures and to practical problems not properly solved yet the problem of image reconstruction and restoration is first formulated some of the current regularization approaches used to solve the problem are then described the concepts of a priori information and compound criterion are introduced a bayesian interpretation of the regularization techniques is given which clarifies the role of the tuning parameters and indicates how they could be estimated the practical aspects of computing the solution first when the hyperparameters are known and second when they must be estimated are then considered conclusions are drawn and points that still need to be investigated are outlined

localization of realistic cortical activity in meg using current multipoles abstract we present a novel approach to meg source estimation based on a regularized first order multipole solution the gaussian regularizing prior is obtained by calculation of the sample mean and covariance matrix for the equivalent moments of realistic simulated cortical activity we compare the regularized multipole localization framework to the classical dipole and general multipole source estimation methods by evaluating the ability of all three solutions to localize the centroids of physiologically plausible patches of activity simulated on the surface of a human cerebral cortex the results obtained with a realistic sensor configuration a spherical head model and given in terms of field and localization error depict the performance of the dipolar and multipolar models as a function of variable source surface area 50 500 mm2 noise conditions 20 10 and 5 db snr source orientation 0 90 and source depth 3 11 cm we show that as the sources increase in size they become less accurately modeled as current dipoles the regularized multipole systematically outperforms the single dipole model increasingly so as the spatial extent of the sources increases in addition our simulations demonstrate that as the orientation of the sources becomes more radial dipole localization accuracy decreases substantially while the performance of the regularized multipole model is far less sensitive to orientation and even succeeds in localizing quasi radial source configurations furthermore our results show that the multipole model is able to localize superficial sources with higher accuracy than the current dipole these results indicate that the regularized multipole solution may be an attractive alternative to current dipole based source estimation methods in meg

differentially amplitude and phase encoded qam for the correlated rayleigh fading channel with diversity reception this paper studies the differentially amplitude and phase encoded dape quadrature amplitude modulation qam transmission over correlated rayleigh channels with diversity reception operating over two successive received symbols the optimum and an asymptotic maximum likelihood aml differentially coherent receiver are developed and compared with a conventional switched diversity combining sdc grid receiver it is shown that the aml and sdc grid receivers are much simpler in complexity than the optimum receiver in that no channel side information is required in their realization an exact expression of the bit error probability bep is obtained for the sdc grid receiver based on a union bound argument a bep upper bound for the aml receiver is also derived and verified by simulation numerical results on 16 and 64 point constellations show that the aml receiver exhibits an almost optimum performance and the sdc grid receiver with a small level of diversity is nearly optimum it is also shown by simulation that the conventional equal gain diversity receiver is almost optimal for demodulating a 16 point dape qam signal

a systematic approach to the design and analysis of optimum dpsk receivers for generalized diversity communications over rayleigh fading channels a generalized diversity channel is introduced that models a variety of wireless communication systems that use time frequency multipath and or antenna diversity with various interbranch correlations between signaling waveforms and the fading and additive noise processes in the context of this general model a systematic approach to the design and analysis of optimum noncoherent differential phase shift keying dpsk receivers is introduced in particular it is shown how the minimum error probability mep and the generalized likelihood ratio tests glrt can be applied to obtain optimal noncoherent combining rules a comparative error rate analysis of the glrt and mep detectors and an ad hoc equal gain combiner is provided for binary signaling and the suitability of the three schemes is determined as a function of fading characteristics the asymptotic bit error rate analysis is undertaken for the mep detector for slow and fast fading channels an estimator detector decomposition of the noncoherent mep rule is obtained which allows an insightful comparative study of the fundamental limits of binary phase shift keying and dpsk modulation detection methods for both slow and fast fading the results of this paper are also applicable to postdecorrelative receivers in multiuser channels

multiple symbol differential detection of mpsk a differential detection technique for mpsk multiple phase shift keying which uses a multiple symbol observation interval is presented and its performance is analyzed and simulated the technique makes use of maximum likelihood sequence estimation of the transmitted phases rather than symbol by symbol detection as in the conventional differential detection thus the performance of this multiple symbol detection scheme fills the gap between conventional two symbol observation differentially coherent detection of mpsk and ideal coherent of mpsk with differential encoding the amount of improvement gained over conventional differential detection depends on the number of phases m and the number of additional symbol intervals added to the observation what is particularly interesting is that substantial performance improvement can be obtained for only one or two additional symbol intervals of observation the analysis and simulation results presented are for uncoded mpsk

improving noise robustness in subspace based joint sparse recovery in a multiple measurement vector problem mmv where multiple signals share a common sparse support and are sampled by a common sensing matrix we can expect joint sparsity to enable a further reduction in the number of required measurements while a diversity gain from joint sparsity had been demonstrated earlier in the case of a convex relaxation method using an l1 l2 mixed norm penalty only recently was it shown that similar diversity gain can be achieved by greedy algorithms if we combine greedy steps with a music like subspace criterion however the main limitation of these hybrid algorithms is that they often require a large number of snapshots or a high signal to noise ratio snr for an accurate subspace as well as partial support estimation one of the main contributions of this work is to show that the noise robustness of these algorithms can be significantly improved by allowing sequential subspace estimation and support filtering even when the number of snapshots is insufficient numerical simulations show that a novel sequential compressive music sequential cs music that combines the sequential subspace estimation and support filtering steps significantly outperforms the existing greedy algorithms and is quite comparable with computationally expensive state of art algorithms

localization of extended brain sources from eeg meg the exso music approach abstract we propose a new music like method called 2q exso music q 1 this method is an extension of the 2q music q 1 approach for solving the eeg meg inverse problem when spatially extended neocortical sources exso are considered it introduces a novel exso music principle the novelty is two fold i the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii the introduction of an original efficient and low cost way of optimizing this metric in 2q exso music the possible use of higher order statistics q 2 offers a better robustness with respect to gaussian noise of unknown spatial coherence and modeling errors as a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a gaussian and spatially correlated noise and the modeling errors induced by the non exact resolution of the forward problem computer results on simulated eeg signals obtained with physiologically relevant models of both the sources and the volume conductor show a highly increased performance of our 2q exso music method as compared to the classical 2q music algorithms

data driven parceling and entropic inference in meg in amblard et al amblard c lapalme e lina j m 2004 biomagnetic source detection by maximum entropy and graphical models ieee trans biomed eng 55 3 427 442 the authors introduced the maximum entropy on the mean mem as a methodological framework for solving the magnetoencephalography meg inverse problem the main component of the mem is a reference probability density that enables one to include all kind of prior information on the source intensity distribution to be estimated this reference law also encompasses the definition of a model we consider a distributed source model together with a clustering hypothesis that assumes functionally coherent dipoles the reference probability distribution is defined as a prior parceling of the cortical surface in this paper we present a data driven approach for parceling out the cortex into regions that are functionally coherent based on the recently developed multivariate source pre localization msp principle mattout j pelegrini issac m garnero l benali h 2005 multivariate source pre localization msp use of functionally informed basis functions for better conditioning the meg inverse problem neuroimage 26 2 356 373 the data driven clustering ddc of the dipoles provides an efficient parceling of the sources as well as an estimate of parameters of the initial reference probability distribution on meg simulated data the ddc is shown to further improve the mem inverse approach as evaluated considering two different iterative algorithms and using classical error metrics as well as roc receiver operating characteristic curve analysis the mem solution is also compared to a loreta like inverse approach the data driven clustering allows to take most advantage of the mem formalism its main trumps lie in the flexible probabilistic way of introducing priors and in the notion of spatial coherent regions of activation the latter reduces the dimensionality of the problem in so doing it narrows down the gap between the two types of inverse methods the popular dipolar approaches and the distributed ones

source localization using recursively applied and projected rap music a new method for source localization is described that is based on a modification of the well known music algorithm in classical music the array manifold vector is projected onto an estimate of the signal subspace errors in the estimate of the signal subspace can make localization of multiple sources difficult recursively applied and projected rap music uses each successively located source to form an intermediate array gain matrix and projects both the array manifold and the signal subspace estimate into its orthogonal complement the music projection to find the next source is then performed in this reduced subspace special assumptions about the array manifold structure such as vandermonde or shift invariance are not required using the metric of principal angles we describe a general form of the rap music algorithm for the case of diversely polarized sources through a uniform linear array simulation with two highly correlated sources we demonstrate the improved monte carlo error performance of rap music relative to music and two other sequential subspace methods s and ies music we then demonstrate the more general utility of this algorithm for multidimensional array manifolds in a magnetoencephalography meg source localization simulation

optimal decoding of coded psk and qam signals in correlated fast fading channels and awgn a combined envelope multiple differential and coherent detection approach the maximum likelihood sequence estimator for the reception of coded digital phase modulated signals with single or multiamplitude constellations transmitted over a multiplicative frequency nonselective i e flat correlated fast fading rayleigh or rician channel and corrupted by additive white gaussian noise awgn is derived due to this correlation the errors caused by fading tend to occur in bursts in the analysis no assumption simplifying the problem is made for fast fading the authors consider the most general case where both phase and amplitude distortion resulting from the fading process could change significantly and thus cannot be assumed to be constant over a number of transmitted symbols it is shown that the estimator s hardware structure consists of a combination of envelope multiple differential and coherent detectors with multiple differential detectors they define a receiver structure consisting of a combination of more than one distinct differential detectors each of them employing a progressively increasing by the symbol duration time delay element the outputs of these detectors are jointly processed by means of an algorithm which is presented in a recursive form the derivation of this new receiver is general enough to accommodate trellis coded phase shift keying psk and quadrature amplitude modulated qam systems differentially encoded signals such as the spl pi 4 shift differential quadrature phase shift keying dqpsk scheme can also be incorporated in order to reduce the overall receiver implementation complexity several reduced complexity near optimal versions of the algorithm are presented these reduced complexity receivers are based on the use of only a few multiple differential detectors performance evaluation results for reduced complexity trellis coded spl pi 4 shift dqpsk spl pi 4 shift 8 dqam differential quadrature amplitude modulation and 8 dpsk differential phase shift keying systems have demonstrated that the proposed receivers significantly reduce the error floors caused by fading

on the performance of signal subspace processing part i narrow band systems this paper presents an analytical evaluation of detection determination of the number of sources and estimation performances of narrow band signal subspace processing for multiple source direction finding the probabilities of underestimating and overestimating the number of sources are derived under asymptotic conditions and around the threshold regions in terms of the choice of a penalty function and signal noise and array parameters for the cases of at most two closely spaced sources in the spatially white noise a scalar measure is introduced for the evaluation of the quality of the estimated signal subspace based on the statistics of this measure performance thresholds are demonstrated for the signal to noise ratio angle separation and correlation between two equipowered sources

combining sparsity and rotational invariance in eeg meg source reconstruction we introduce focal vector field reconstruction fvr a novel technique for the inverse imaging of vector fields the method was designed to simultaneously achieve two goals a invariance with respect to the orientation of the coordinate system and b a preference for sparsity of the solutions and their spatial derivatives this was achieved by defining the regulating penalty function which renders the solutions unique as a global l 1 norm of local l 2 norms we show that the method can be successfully used for solving the eeg inverse problem in the joint localization of 2 3 simulated dipoles fvr always reliably recovers the true sources the competing methods have limitations in distinguishing close sources because their estimates are either too smooth loreta minimum l 1 norm or too scattered minimum l 2 norm in both noiseless and noisy simulations fvr has the smallest localization error according to the earth mover s distance emd which is introduced here as a meaningful measure to compare arbitrary source distributions we also apply the method to the simultaneous localization of left and right somatosensory n20 generators from real eeg recordings compared to its peers fvr was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge

testing for nonlinearity in time series the method of surrogate data we describe a statistical approach for identifying nonlinearity in time series the method first specifies some linear process as a null hypothesis then generates surrogate data sets which are consistent with this null hypothesis and finally computes a discriminating statistic for the original and for each of the surrogate data sets if the value computed for the original data is significantly different than the ensemble of values computed for the surrogate data then the null hypothesis is rejected and nonlinearity is detected we discuss various null hypotheses and discriminating statistics the method is demonstrated for numerical data generated by known chaotic systems and applied to a number of experimental time series which arise in the measurement of superfluids brain waves and sunspots we evaluate the statistical significance of the evidence for nonlinear structure in each case and illustrate aspects of the data which this approach identifies

meg source localization under multiple constraints an extended bayesian framework reconstructing the sources of magnetoencephalographic meg measurements is an ill posed inverse problem which does not admit a unique solution forward modeling of the observed signal and prior information or constraints on the solution are needed many types of constraints have been considered however a general framework is required that can accommodate for multiple priors and enables the evaluation of their contribution with this in mind we developed an empirical bayesian framework based on hierarchical linear models our approach entails the estimation of both the expected source distribution and its conditional variance the latter being constrained by an empirically determined mixture of prior variance components corresponding to the constraints one wants to introduce finally a second level of inference based on bayesian model selection enables one to compare different combinations of priors we describe here the methodological framework the estimation scheme and illustrate its application on a single subject meg data set

application of multi source minimum variance beamformers for reconstruction of correlated neural activity linearly constrained minimum variance beamformers are highly effective for analysis of weakly correlated brain activity but their performance degrades when correlations become significant multiple constrained minimum variance mcmv beamformers are insensitive to source correlations but require a priori information about the source locations besides the question whether unbiased estimates of source positions and orientations can be obtained remained unanswered in this work we derive mcmv based source localizers that can be applied to both induced and evoked brain activity they may be regarded as a generalization of scalar minimum variance beamformers for the case of multiple correlated sources we show that for arbitrary noise covariance these beamformers provide simultaneous unbiased estimates of multiple source positions and orientations and remain bounded at singular points we also propose an iterative search algorithm that makes it possible to find sources approximately without a priori assumptions about their locations and orientations simulations and analyses of real meg data demonstrate that presented approach is superior to traditional single source beamformers in situations where correlations between the sources are significant

multiresolution imaging of meg cortical sources using an explicit piecewise model imaging neural generators from meg magnetic fields is often considered as a compromise between computationally reasonable methodology that usually yields poor spatial resolution on the one hand and more sophisticated approaches on the other hand potentially leading to intractable computational costs we approach the problem of obtaining well resolved source images with unexcessive computation load with a multiresolution image model selection mims technique the building blocks of the mims source model are parcels of the cortical surface which can be designed at multiple spatial resolutions with the combination of anatomical and functional priors computation charge is reduced owing to 1 compact parametric models of the activation of extended brain parcels using current multipole expansions and 2 the optimization of the generalized cross validation error on image models which is closed form for the broad class of linear estimators of neural currents model selection can be complemented by any conventional imaging approach of neural currents restricted to the optimal image support obtained from mims the estimation of the location and spatial extent of brain activations is discussed and evaluated using extensive monte carlo simulations an experimental evaluation was conducted with meg data from a somatotopic paradigm results show that mims is an efficient image model selection technique with robust performances at realistic noise levels

decision feedback differential detection of differentially encoded 16apsk signals multiple symbol differential detection dd with reference signal estimation based on the feedback of past detected symbols is presented for differentially encoded 16 level amplitude phase shift keying 16dapsk a suboptimal decision is derived the approximate analysis of the bit error rate ber performance taking into account the decision error propagation effect is presented in an additive white gaussian noise awgn channel and the ber performance is compared with those of 16dpsk and 16 quadrature amplitude modulation 16qam

maximum likelihood differential detection of uncoded and trellis coded amplitude phase modulation over awgn and fading channels spl minus metrics and performance this paper derives metrics for maximum likelihood differential detection of uncoded and trellis coded mpsk and qam transmitted over rayleigh and rician fading channels receiver structures based on these metrics are proposed and their error probability performance analyzed and or simulated the results represent a generalization of the notion of multiple symbol differential detection previously introduced by the authors for mpsk over an awgn to the fading channel and other modulations for the coded cases ideal interleaving deinterleaving is assumed and furthermore the presence or absence of channel state information an interesting side result is that for a constant envelope modulation transmitted over a fading channel with unknown but rapidly varying phase error the other extreme to the slowly varying phase error case normally assumed for differential detection under certain practical assumptions it is shown that the optimum receiver is of the limiter discriminator type

time coherent expansion of meg eeg cortical sources in this study we estimated the spatial extent of cortical areas of time coherent activity using the inverse problem in magneto electroencephalography meeg the model discussed here uses classical regularization tools in order to force the inverse solution to be piecewise coherent first the cortex was seeded by focal dipolar sources then a time coherent expansion tce onto the cortical surface was performed in order to obtain surface source models composed of patches with uniform current density patches represent extended cortical regions with one single time course per active area results obtained from synthetic data show that using the tce method is relevant even with a low signal to noise ratio although the final estimation is often slightly biased we applied the tce method to evoked magnetic fields obtained after electrical stimulation of fingers in order to estimate the somatotopic cortical maps of the primary somatosensory cortex

a theory for multiresolution signal decomposition the wavelet representation multiresolution representations are effective for analyzing the information content of images the properties of the operator which approximates a signal at a given resolution were studied it is shown that the difference of information between the approximation of a signal at the resolutions 2 sup j 1 and 2 sup j where j is an integer can be extracted by decomposing this signal on a wavelet orthonormal basis of l sup 2 r sup n the vector space of measurable square integrable n dimensional functions in l sup 2 r a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi x this decomposition defines an orthogonal multiresolution representation called a wavelet representation it is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters wavelet representation lies between the spatial and fourier domains for images the wavelet representation differentiates several spatial orientations the application of this representation to data compression in image coding texture discrimination and fractal analysis is discussed