References of "Bugli, Céline"
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See detailComparison between principal component analysis and independent component analysis in EEG modelling
Bugli, Céline; Lambert, Philippe ULg

in Biometrical Journal = Biometrische Zeitschrift (2007), 49

Principal Component Analysis (PCA) is a classical technique in statistical data analysis, feature extraction and data reduction, aiming at explaining observed signals as a linear combination of orthogonal ... [more ▼]

Principal Component Analysis (PCA) is a classical technique in statistical data analysis, feature extraction and data reduction, aiming at explaining observed signals as a linear combination of orthogonal principal components. Independent Component Analysis (ICA) is a technique of array processing and data analysis, aiming at recovering unobserved signals or ‘sources’ from observed mixtures, exploiting only the assumption of mutual independence between the signals. The separation of the sources by ICA has great potential in applications such as the separation of sound signals (like voices mixed in simultaneous multiple records, for example), in telecommunication or in the treatment of medical signals. However, ICA is not yet often used by statisticians. In this paper, we shall present ICA in a statistical framework and compare this method with PCA for electroencephalograms (EEG) analysis.We shall see that ICA provides a more useful data representation than PCA, for instance, for the representation of a particular characteristic of the EEG named event-related potential (ERP). [less ▲]

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See detailFunctional ANOVA with random functional effects: an application to event-related potentials modelling for electroencephalograms analysis
Bugli, Céline; Lambert, Philippe ULg

in Statistics in Medicine (2006), 25

The di erential e ects of basic visual or auditory stimuli on electroencephalograms (EEG), named event related potentials (ERPs), are often used to evaluate the impact of treatments on brain performances ... [more ▼]

The di erential e ects of basic visual or auditory stimuli on electroencephalograms (EEG), named event related potentials (ERPs), are often used to evaluate the impact of treatments on brain performances. In the present paper, we propose a P-splines based model that can be used to evaluate treatment e ect on the timing and the amplitude of some peaks of the ERPs curves. Functional ANOVA is an adaptation of linear model or analysis of variance to analyse functional observations. The changes in the functional of interest e ects are generally described using smoothing splines. Eilers and Marx proposed to work with P-splines, a combination of B-splines and di erence penalties on coe cients. We de ne a Psplines model for ERPs curves combined with random e ects. In particular, we show that it is a useful alternative to classical strategies requiring the visual and usually imprecise localization of speci c ERP peaks from curves with a low signal-to-noise ratio. [less ▲]

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See detailDiscrimination of shifts in a soil microbial community assosciated with TNT-contamination using a functional ANOVA of 16S rRNA hybridized to oligonucleotide microarrays
Eyers; Smoot, J. C.; Smoot, L. M. et al

in Environmental Science & Technology (2006), 40

A functional ANOVA analysis of the thermal dissociation of RNA hybridized to DNA microarrays was used to improve discrimination between two soil microbial communities. Following hybridization of in vitro ... [more ▼]

A functional ANOVA analysis of the thermal dissociation of RNA hybridized to DNA microarrays was used to improve discrimination between two soil microbial communities. Following hybridization of in vitro transcribed 16S rRNA derived from uncontaminated and 2,4,6- trinitrotoluene contaminated soils to an oligonucleotide microarray containing group- and species-specific perfect match (PM) probes and mismatch (MM) variants, thermal dissociation was used to analyze the nucleic acid bound to each PM-MM probe set. Functional ANOVA of the dissociation curves generally discriminated PM-MM probe sets when Td values (temperature at 50% probe-target dissociation) could not. Maximum discrimination for many PM and MM probes often occurred at temperatures greater than theTd. Comparison of signal intensities measured prior to dissociation analysis from hybridizations of the two soil samples revealed significant differences in domain-, group-, and species-specific probes. Functional ANOVA showed significantly different dissociation curves for 11 PM probes when hybridizations from the two soil samples were compared, even though initial signal intensities for 3 of the 11 did not vary. [less ▲]

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