16,163 research outputs found
Modeling sparse connectivity between underlying brain sources for EEG/MEG
We propose a novel technique to assess functional brain connectivity in
EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA),
can overcome the problem of volume conduction by modeling neural data
innovatively with the following ingredients: (a) the EEG is assumed to be a
linear mixture of correlated sources following a multivariate autoregressive
(MVAR) model, (b) the demixing is estimated jointly with the source MVAR
parameters, (c) overfitting is avoided by using the Group Lasso penalty. This
approach allows to extract the appropriate level cross-talk between the
extracted sources and in this manner we obtain a sparse data-driven model of
functional connectivity. We demonstrate the usefulness of SCSA with simulated
data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure
Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data
Context. The direct detection of exoplanets with high-contrast imaging
requires advanced data processing methods to disentangle potential planetary
signals from bright quasi-static speckles. Among them, angular differential
imaging (ADI) permits potential planetary signals with a known rotation rate to
be separated from instrumental speckles that are either statics or slowly
variable. The method presented in this paper, called ANDROMEDA for ANgular
Differential OptiMal Exoplanet Detection Algorithm is based on a maximum
likelihood approach to ADI and is used to estimate the position and the flux of
any point source present in the field of view. Aims. In order to optimize and
experimentally validate this previously proposed method, we applied ANDROMEDA
to real VLT/NaCo data. In addition to its pure detection capability, we
investigated the possibility of defining simple and efficient criteria for
automatic point source extraction able to support the processing of large
surveys. Methods. To assess the performance of the method, we applied ANDROMEDA
on VLT/NaCo data of TYC-8979-1683-1 which is surrounded by numerous bright
stars and on which we added synthetic planets of known position and flux in the
field. In order to accommodate the real data properties, it was necessary to
develop additional pre-processing and post-processing steps to the initially
proposed algorithm. We then investigated its skill in the challenging case of a
well-known target, Pictoris, whose companion is close to the detection
limit and we compared our results to those obtained by another method based on
principal component analysis (PCA). Results. Application on VLT/NaCo data
demonstrates the ability of ANDROMEDA to automatically detect and characterize
point sources present in the image field. We end up with a robust method
bringing consistent results with a sensitivity similar to the recently
published algorithms, with only two parameters to be fine tuned. Moreover, the
companion flux estimates are not biased by the algorithm parameters and do not
require a posteriori corrections. Conclusions. ANDROMEDA is an attractive
alternative to current standard image processing methods that can be readily
applied to on-sky data
Dynamical spectral unmixing of multitemporal hyperspectral images
In this paper, we consider the problem of unmixing a time series of
hyperspectral images. We propose a dynamical model based on linear mixing
processes at each time instant. The spectral signatures and fractional
abundances of the pure materials in the scene are seen as latent variables, and
assumed to follow a general dynamical structure. Based on a simplified version
of this model, we derive an efficient spectral unmixing algorithm to estimate
the latent variables by performing alternating minimizations. The performance
of the proposed approach is demonstrated on synthetic and real multitemporal
hyperspectral images.Comment: 13 pages, 10 figure
Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling
Solving linear regression problems based on the total least-squares (TLS)
criterion has well-documented merits in various applications, where
perturbations appear both in the data vector as well as in the regression
matrix. However, existing TLS approaches do not account for sparsity possibly
present in the unknown vector of regression coefficients. On the other hand,
sparsity is the key attribute exploited by modern compressive sampling and
variable selection approaches to linear regression, which include noise in the
data, but do not account for perturbations in the regression matrix. The
present paper fills this gap by formulating and solving TLS optimization
problems under sparsity constraints. Near-optimum and reduced-complexity
suboptimum sparse (S-) TLS algorithms are developed to address the perturbed
compressive sampling (and the related dictionary learning) challenge, when
there is a mismatch between the true and adopted bases over which the unknown
vector is sparse. The novel S-TLS schemes also allow for perturbations in the
regression matrix of the least-absolute selection and shrinkage selection
operator (Lasso), and endow TLS approaches with ability to cope with sparse,
under-determined "errors-in-variables" models. Interesting generalizations can
further exploit prior knowledge on the perturbations to obtain novel weighted
and structured S-TLS solvers. Analysis and simulations demonstrate the
practical impact of S-TLS in calibrating the mismatch effects of contemporary
grid-based approaches to cognitive radio sensing, and robust
direction-of-arrival estimation using antenna arrays.Comment: 30 pages, 10 figures, submitted to IEEE Transactions on Signal
Processin
Mapping multivariate measures of brain response onto stimulus information during emotional face classification
The relationship between feature processing and visual classification in the brain has been explored through a combination of reverse correlation methods (i.e.“Bubbles” [22]) and electrophysiological measurements (EEG) taken during a facial emotion categorization task [63]. However, in the absence of any specific model of the brain response measurements, this and other [60] attempts to parametrically relate stimulus properties to measurements of brain activation are difficult to interpret. In this thesis I consider a blind data–driven model of brain response. Statistically independent model parameters are found to minimize the expectation of an objective likelihood function over time [55], and a novel combination of methods is proposed for separating the signal from the noise. The model’s estimated signal parameters are then objectively rated by their ability to explain the subject’s performance during a facial emotion classification task, and also by their ability to explain the stimulus features, as revealed in a Bubbles experiment
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