1,792 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
Least Dependent Component Analysis Based on Mutual Information
We propose to use precise estimators of mutual information (MI) to find least
dependent components in a linearly mixed signal. On the one hand this seems to
lead to better blind source separation than with any other presently available
algorithm. On the other hand it has the advantage, compared to other
implementations of `independent' component analysis (ICA) some of which are
based on crude approximations for MI, that the numerical values of the MI can
be used for:
(i) estimating residual dependencies between the output components;
(ii) estimating the reliability of the output, by comparing the pairwise MIs
with those of re-mixed components;
(iii) clustering the output according to the residual interdependencies.
For the MI estimator we use a recently proposed k-nearest neighbor based
algorithm. For time sequences we combine this with delay embedding, in order to
take into account non-trivial time correlations. After several tests with
artificial data, we apply the resulting MILCA (Mutual Information based Least
dependent Component Analysis) algorithm to a real-world dataset, the ECG of a
pregnant woman.
The software implementation of the MILCA algorithm is freely available at
http://www.fz-juelich.de/nic/cs/softwareComment: 18 pages, 20 figures, Phys. Rev. E (in press
Mixing and non-mixing local minima of the entropy contrast for blind source separation
In this paper, both non-mixing and mixing local minima of the entropy are
analyzed from the viewpoint of blind source separation (BSS); they correspond
respectively to acceptable and spurious solutions of the BSS problem. The
contribution of this work is twofold. First, a Taylor development is used to
show that the \textit{exact} output entropy cost function has a non-mixing
minimum when this output is proportional to \textit{any} of the non-Gaussian
sources, and not only when the output is proportional to the lowest entropic
source. Second, in order to prove that mixing entropy minima exist when the
source densities are strongly multimodal, an entropy approximator is proposed.
The latter has the major advantage that an error bound can be provided. Even if
this approximator (and the associated bound) is used here in the BSS context,
it can be applied for estimating the entropy of any random variable with
multimodal density.Comment: 11 pages, 6 figures, To appear in IEEE Transactions on Information
Theor
Fast and accurate methods of independent component analysis: A survey
summary:This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram (EEG)
Neural networks and separation of Cosmic Microwave Background and astrophysical signals in sky maps
The Independent Component Analysis (ICA) algorithm is implemented as a neural
network for separating signals of different origin in astrophysical sky maps.
Due to its self-organizing capability, it works without prior assumptions on
the signals, neither on their frequency scaling, nor on the signal maps
themselves; instead, it learns directly from the input data how to separate the
physical components, making use of their statistical independence. To test the
capabilities of this approach, we apply the ICA algorithm on sky patches, taken
from simulations and observations, at the microwave frequencies, that are going
to be deeply explored in a few years on the whole sky, by the Microwave
Anisotropy Probe (MAP) and by the {\sc Planck} Surveyor Satellite. The maps are
at the frequencies of the Low Frequency Instrument (LFI) aboard the {\sc
Planck} satellite (30, 44, 70 and 100 GHz), and contain simulated astrophysical
radio sources, Cosmic Microwave Background (CMB) radiation, and Galactic
diffuse emissions from thermal dust and synchrotron. We show that the ICA
algorithm is able to recover each signal, with precision going from 10% for the
Galactic components to percent for CMB; radio sources are almost completely
recovered down to a flux limit corresponding to , where
is the rms level of CMB fluctuations. The signal recovering
possesses equal quality on all the scales larger then the pixel size. In
addition, we show that the frequency scalings of the input signals can be
partially inferred from the ICA outputs, at the percent precision for the
dominant components, radio sources and CMB.Comment: 15 pages; 6 jpg and 1 ps figures. Final version to be published in
MNRA
A review of blind source separation in NMR spectroscopy
27 pagesInternational audienceFourier transform is the data processing naturally associated to most NMR experiments. Notable exceptions are Pulse Field Gradient and relaxation analysis, the structure of which is only partially suitable for FT. With the revamp of NMR of complex mixtures, fueled by analytical challenges such as metabolomics, alternative and more apt mathematical methods for data processing have been sought, with the aim of decomposing the NMR signal into simpler bits. Blind source separation is a very broad definition regrouping several classes of mathematical methods for complex signal decomposition that use no hypothesis on the form of the data. Developed outside NMR, these algorithms have been increasingly tested on spectra of mixtures. In this review, we shall provide an historical overview of the application of blind source separation methodologies to NMR, including methods specifically designed for the specificity of this spectroscopy
Bayesian separation of spectral sources under non-negativity and full additivity constraints
This paper addresses the problem of separating spectral sources which are
linearly mixed with unknown proportions. The main difficulty of the problem is
to ensure the full additivity (sum-to-one) of the mixing coefficients and
non-negativity of sources and mixing coefficients. A Bayesian estimation
approach based on Gamma priors was recently proposed to handle the
non-negativity constraints in a linear mixture model. However, incorporating
the full additivity constraint requires further developments. This paper
studies a new hierarchical Bayesian model appropriate to the non-negativity and
sum-to-one constraints associated to the regressors and regression coefficients
of linear mixtures. The estimation of the unknown parameters of this model is
performed using samples generated using an appropriate Gibbs sampler. The
performance of the proposed algorithm is evaluated through simulation results
conducted on synthetic mixture models. The proposed approach is also applied to
the processing of multicomponent chemical mixtures resulting from Raman
spectroscopy.Comment: v4: minor grammatical changes; Signal Processing, 200
Independent Component Analysis Enhancements for Source Separation in Immersive Audio Environments
In immersive audio environments with distributed microphones, Independent Component Analysis (ICA) can be applied to uncover signals from a mixture of other signals and noise, such as in a cocktail party recording. ICA algorithms have been developed for instantaneous source mixtures and convolutional source mixtures. While ICA for instantaneous mixtures works when no delays exist between the signals in each mixture, distributed microphone recordings typically result various delays of the signals over the recorded channels. The convolutive ICA algorithm should account for delays; however, it requires many parameters to be set and often has stability issues. This thesis introduces the Channel Aligned FastICA (CAICA), which requires knowledge of the source distance to each microphone, but does not require knowledge of noise sources. Furthermore, the CAICA is combined with Time Frequency Masking (TFM), yielding even better SOI extraction even in low SNR environments. Simulations were conducted for ranking experiments tested the performance of three algorithms: Weighted Beamforming (WB), CAICA, CAICA with TFM. The Closest Microphone (CM) recording is used as a reference for all three. Statistical analyses on the results demonstrated superior performance for the CAICA with TFM. The algorithms were applied to experimental recordings to support the conclusions of the simulations. These techniques can be deployed in mobile platforms, used in surveillance for capturing human speech and potentially adapted to biomedical fields
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