References of "Kirkove, Murielle"
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See detailEvaluation of the performance of an experimental somnolence quantification system in terms of reaction times and lapses
François, Clémentine ULg; Wertz, Jérôme ULg; Kirkove, Murielle ULg et al

in Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2014, August)

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See detailComparative evaluation of existing and new methods for correcting ocular artifacts in electroencephalographic recordings
Kirkove, Murielle ULg; François, Clémentine ULg; Verly, Jacques ULg

in Signal Processing (2014), 98(C), 102-120

EEG signals are often contaminated by ocular artifacts (OAs), in particular when they are recorded for a subject that is, in principle, awake, such as in a study of drowsiness. It is generally desirable ... [more ▼]

EEG signals are often contaminated by ocular artifacts (OAs), in particular when they are recorded for a subject that is, in principle, awake, such as in a study of drowsiness. It is generally desirable to detect and/or correct these OAs before interpreting the EEG signals. We have identified 11 existing methods for dealing with OAs. Their study allowed us to create 16 new methods. We performed a comparative performance evaluation of the resulting 27 distinct methods using a common set of data and a common set of metrics. The data was recorded during a driving task of about two hours in a driving simulator. This led to a ranking of all methods, with five emerging clear winners, comprising two existing methods and three new ones. [less ▲]

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See detailPerformance evaluation of methods for correcting ocular artifacts in electroencephalographic (EEG) recordings
Kirkove, Murielle ULg; François, Clémentine ULg; Libotte, Aurélie et al

Conference (2013, February)

The presence of ocular artifacts (OA) due to eye movements and eye blinks is a major problem for the analysis of electroencephalographic (EEG) recordings in most applications. A large variety of methods ... [more ▼]

The presence of ocular artifacts (OA) due to eye movements and eye blinks is a major problem for the analysis of electroencephalographic (EEG) recordings in most applications. A large variety of methods (algorithms) exist for detecting or/and correcting OA’s. We identified the most promising methods, implemented them, and compared their performance for correctly detecting the presence of OA’s. These methods are based on signal processing “tools” that can be classified into three categories: wavelet transform, adaptive filtering, and blind source separation. We evaluated the methods using EEG signals recorded from three healthy persons subjected to a driving task in a driving simulator. We performed a thorough comparison of the methods in terms of the usual performances measures (sensitivity, specificity, and ROC curves), using our own manual scoring of the recordings as ground truth. Our results show that methods based on adaptive filtering such as LMS and RLS appear to be the best to successfully identify OA’s in EEG recordings. [less ▲]

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See detailContrast and sensitivity performances of Elscint, General Electric, Siemens and SMV multi-heads cameras.
Kirkove, Murielle ULg; Seret, Alain ULg

in European Journal of Nuclear Medicine and Molecular Imaging (2007, October), 34(S2), 345

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See detailComparaison de techniques de débruitage des images scintigraphiques
Kirkove, Murielle ULg; Seret, Alain ULg

in Médecine nucléaire (2007), 31(5), 219-234

Scintigraphic images are strongly affected by Poisson noise. This article presents the results of a comparison between denoising methods for Poisson noise according to different criteria: the gain in ... [more ▼]

Scintigraphic images are strongly affected by Poisson noise. This article presents the results of a comparison between denoising methods for Poisson noise according to different criteria: the gain in signal-to-noise ratio, the preservation of resolution and contrast, and the visual quality. The wavelet techniques recently developed to denoise Poisson noise limited images are divided into two groups based on: (1) the Haar representation, (2) the transformation of Poisson noise into white Gaussian noise by the Haar–Fisz transform followed by a denoising. In this study, three variants of the first group and three variants of the second, including the adaptative Wiener filter, four types of wavelet thresholdings and the Bayesian method of Pizurica were compared to Metz and Hanning filters and to Shine, a systematic noise elimination process. All these methods, except Shine, are parametric. For each of them, ranges of optimal values for the parameters were highlighted as a function of the aforementioned criteria. The intersection of ranges for the wavelet methods without thresholding was empty, and these methods were therefore not further compared quantitatively. The thresholding techniques and Shine gave the best results in resolution and contrast. The largest improvement in signal-to-noise ratio was obtained by the filters. Ideally, these filters should be accurately defined for each image. This is difficult in the clinical context. Moreover, they generate oscillation artefacts. In addition, the wavelet techniques did not bring significant improvements, and are rather slow. Therefore, Shine, which is fast and works automatically, appears to be an interesting alternative. [less ▲]

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See detailComparison of the current techniques used for the denoising of scintigraphic images
Kirkove, Murielle ULg; Seret, Alain ULg

in European Journal of Nuclear Medicine and Molecular Imaging (2006), 33(S2), 318

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See detailA Method for Spatial Deconvolution of Spectra
Courbin, F.; Magain, Pierre ULg; Kirkove, Murielle ULg et al

in Astrophysical Journal (2000), 529

A method for spatial deconvolution of spectra is presented. It follows the same fundamental principles as the ``MCS image deconvolution algorithm'' recently developed by Magain, Courbin, & Sohy and uses ... [more ▼]

A method for spatial deconvolution of spectra is presented. It follows the same fundamental principles as the ``MCS image deconvolution algorithm'' recently developed by Magain, Courbin, & Sohy and uses information contained in the spectrum of a reference point-spread function to spatially deconvolve spectra of very blended sources. An improved resolution rather than an infinite one is aimed at, overcoming the well-known problem of ``deconvolution artifacts.'' As in the MCS algorithm, the data are decomposed into a sum of analytical point sources and a numerically deconvolved background so that the spectrum of extended sources in the immediate vicinity of bright point sources may be accurately extracted and sharpened. The algorithm has been tested on simulated data including seeing variation as a function of wavelength and atmospheric refraction. It is shown that the spectra of severely blended point sources can be resolved while fully preserving the spectrophotometric properties of the data. Extended objects ``hidden'' by bright point sources (up to 4-5 mag brighter) can be accurately recovered as well, provided the data have a sufficiently high total signal-to-noise ratio (200-300 per spectral resolution element). Such spectra are relatively easy to obtain, even down to faint magnitudes, within a few hours of integration time with 10 m class telescopes. [less ▲]

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