109 research outputs found
SSA of biomedical signals: A linear invariant systems approach
Singular spectrum analysis (SSA) is considered from a linear invariant systems perspective. In this terminology, the extracted components are considered as outputs of a linear invariant system which corresponds to finite impulse response (FIR) filters. The number of filters is determined by the embedding dimension.We propose to explicitly define the frequency response of each filter responsible for the selection of informative components. We also introduce a subspace distance measure for clustering subspace models. We illustrate the methodology by analyzing lectroencephalograms (EEG).FCT - PhD scholarship (SFRH/BD/28404/2006)FCT - PhD scholarship (SFRH/BD/48775/2008
Identifying evoked potential response patterns using independent component analysis and unsupervised learning
Independent Component Analysis(ICA) is a pre-processing step widely used in brain studies. One of
the most common problems in artifact elimination or brain activity related studies is the ordering and
identification of the independent components(ICs). In this work, a novel procedure is proposed
which combines ICA decomposition at trial level with an unsupervised learning algorithm (K-means)
at participant level in order to enhance the related signal patterns which might represent interesting
brain waves. The feasibility of this methodology is evaluated with EEG data acquired with participants
performing on the Halstead Category Test. The analysis shows that it is possible to find the Feedback
Error Negativity (FRN) Potential at single-trial level and relate its characteristics with the performance
of the participant based on their knowledge of the abstract principle underlying the task.info:eu-repo/semantics/publishedVersio
Linear Invariant Systems Theory for Signal Enhancement
This paper discusses a linear time invariant (LTI) systems approach to signal enhancement via projective subspace techniques. It provides closed form expressions for the frequency response of data adaptive finite impulse response eigenfilters. An illustrative example using speech enhancement is also presented.Este artigo apresenta a aplicação da teoria de sistemas lineares invariantes no tempo (LTI) na análise de técnicas de sub-espaço. A resposta em frequência dos filtros resultantes da decomposição em valores singulares é obtida aplicando as propriedades dos sistemas LTI
dAMUSE : a new tool for denoising and blind source separation
In this work a generalized version of AMUSE, called dAMUSE is proposed. The main modification consists in embedding the observed mixed signals in a high-dimensional feature space of delayed
coordinates. With the embedded signals a matrix pencil is formed and its generalized eigendecomposition is computed similar to the algorithm AMUSE. We show that in this case the uncorrelated
output signals are filtered versions of the unknown source signals. Further, denoising the data can be
achieved conveniently in parallel with the signal separation. Numerical simulations using artificially
mixed signals are presented to show the performance of the method. Further results of a heart rate
variability (HRV) study are discussed showing that the output signals are related with LF (low frequency) and HF (high frequency) fluctuations. Finally, an application to separate artifacts from 2D
NOESY NMR spectra and to denoise the reconstructed artefact-free spectra is presented also.info:eu-repo/semantics/publishedVersio
Quasi-stationary distributions for the Domany-Kinzel stochastic cellular automaton
We construct the {\it quasi-stationary} (QS) probability distribution for the
Domany-Kinzel stochastic cellular automaton (DKCA), a discrete-time Markov
process with an absorbing state. QS distributions are derived at both the one-
and two-site levels. We characterize the distribuitions by their mean, and
various moment ratios, and analyze the lifetime of the QS state, and the
relaxation time to attain this state. Of particular interest are the scaling
properties of the QS state along the critical line separating the active and
absorbing phases. These exhibit a high degree of similarity to the contact
process and the Malthus-Verhulst process (the closest continuous-time analogs
of the DKCA), which extends to the scaling form of the QS distribution.Comment: 15 pages, 9 figures, submited to PR
Denoising using local projective subspace methods
In this paper we present denoising algorithms for enhancing noisy signals based on Local ICA (LICA), Delayed AMUSE (dAMUSE)
and Kernel PCA (KPCA). The algorithm LICA relies on applying ICA locally to clusters of signals embedded in a high-dimensional
feature space of delayed coordinates. The components resembling the signals can be detected by various criteria like estimators of
kurtosis or the variance of autocorrelations depending on the statistical nature of the signal. The algorithm proposed can be applied
favorably to the problem of denoising multi-dimensional data. Another projective subspace denoising method using delayed coordinates
has been proposed recently with the algorithm dAMUSE. It combines the solution of blind source separation problems with denoising
efforts in an elegant way and proofs to be very efficient and fast. Finally, KPCA represents a non-linear projective subspace method that
is well suited for denoising also. Besides illustrative applications to toy examples and images, we provide an application of all algorithms
considered to the analysis of protein NMR spectra.info:eu-repo/semantics/publishedVersio
ERP correlates of error processing during performance on the HalsteadCategory Test
The Halstead Category Test (HCT) is a neuropsychological test that measures a person's ability to formulate and
apply abstract principles. Performance must be adjusted based on feedback after each trial and errors are
common until the underlying rules are discovered. Event-related potential (ERP) studies associated with the HCT
are lacking. This paper demonstrates the use of amethodology inspired on Singular SpectrumAnalysis (SSA) applied
to EEG signals, to remove high amplitude ocular andmovement artifacts during performance on the test. This filtering
technique introduces no phase or latency distortions, with minimum loss of relevant EEG information. Importantly,
the test was applied in its original clinical format, without introducing adaptations to ERP recordings. After
signal treatment, the feedback-related negativity (FRN) wave, which is related to error-processing, was identified.
This component peaked around 250ms, after feedback, in fronto-central electrodes. As expected, errors elicited
more negative amplitudes than correct responses. Results are discussed in terms of the increased clinical potential
that coupling ERP informationwith behavioral performance data can bring to the specificity of theHCT in diagnosing
different types of impairment in frontal brain function.info:eu-repo/semantics/publishedVersio
Evidence for a mixed mass composition at the `ankle' in the cosmic-ray spectrum
We report a first measurement for ultra-high energy cosmic rays of the
correlation between the depth of shower maximum and the signal in the water
Cherenkov stations of air-showers registered simultaneously by the fluorescence
and the surface detectors of the Pierre Auger Observatory. Such a correlation
measurement is a unique feature of a hybrid air-shower observatory with
sensitivity to both the electromagnetic and muonic components. It allows an
accurate determination of the spread of primary masses in the cosmic-ray flux.
Up till now, constraints on the spread of primary masses have been dominated by
systematic uncertainties. The present correlation measurement is not affected
by systematics in the measurement of the depth of shower maximum or the signal
in the water Cherenkov stations. The analysis relies on general characteristics
of air showers and is thus robust also with respect to uncertainties in
hadronic event generators. The observed correlation in the energy range around
the `ankle' at differs significantly from
expectations for pure primary cosmic-ray compositions. A light composition made
up of proton and helium only is equally inconsistent with observations. The
data are explained well by a mixed composition including nuclei with mass . Scenarios such as the proton dip model, with almost pure compositions, are
thus disfavoured as the sole explanation of the ultrahigh-energy cosmic-ray
flux at Earth.Comment: Published version. Added journal reference and DOI. Added Report
Numbe
Atmospheric effects on extensive air showers observed with the Surface Detector of the Pierre Auger Observatory
Atmospheric parameters, such as pressure (P), temperature (T) and density,
affect the development of extensive air showers initiated by energetic cosmic
rays. We have studied the impact of atmospheric variations on extensive air
showers by means of the surface detector of the Pierre Auger Observatory. The
rate of events shows a ~10% seasonal modulation and ~2% diurnal one. We find
that the observed behaviour is explained by a model including the effects
associated with the variations of pressure and density. The former affects the
longitudinal development of air showers while the latter influences the Moliere
radius and hence the lateral distribution of the shower particles. The model is
validated with full simulations of extensive air showers using atmospheric
profiles measured at the site of the Pierre Auger Observatory.Comment: 24 pages, 9 figures, accepted for publication in Astroparticle
Physic
The exposure of the hybrid detector of the Pierre Auger Observatory
The Pierre Auger Observatory is a detector for ultra-high energy cosmic rays.
It consists of a surface array to measure secondary particles at ground level
and a fluorescence detector to measure the development of air showers in the
atmosphere above the array. The "hybrid" detection mode combines the
information from the two subsystems. We describe the determination of the
hybrid exposure for events observed by the fluorescence telescopes in
coincidence with at least one water-Cherenkov detector of the surface array. A
detailed knowledge of the time dependence of the detection operations is
crucial for an accurate evaluation of the exposure. We discuss the relevance of
monitoring data collected during operations, such as the status of the
fluorescence detector, background light and atmospheric conditions, that are
used in both simulation and reconstruction.Comment: Paper accepted by Astroparticle Physic
- …