Search results for "dimensionality"

A Nonlinear Approach to Brain Function: Deterministic Chaos and Sleep EEG

1992

In order to perform a nonlinear dimensional analysis of the sleep electroencephalogram (EEG), we applied an algorithm proposed by Grassberger and Procaccia to calculate the correlation dimension D2 of different sleep stages under Lorazepam medication versus placebo. This correlation dimension characterizes the dynamics of the sleep EEG and it estimates the degrees of freedom of the signal under study. We demonstrate that slow-wave sleep depicts a much smaller dimensionality than light or rapid eye movement (REM) sleep, and that Lorazepam does not alter the EEG's dimensionality except in stage II and REM.

Changes in power curve shapes as an indicator of fatigue during dynamic contractions.

2010

The purpose of this study was to analyze exercise-induced leg fatigue during a dynamic fatiguing task by examining the shapes of power vs. time curves through the combined use of several statistical methods: B-spline smoothing, functional principal components and (supervised and unsupervised) classification. In addition, granulometric size distributions were also computed to allow for comparison of curves coming from different subjects. Twelve physically active men participated in one acute heavy-resistance exercise protocol which consisted of five sets of 10 repetition maximum leg press with 120 s of rest between sets. To obtain a smooth and accurate representation of the data, a basis of …

Smoking Behavior: A Cross-Sectional Study to Assess the Dimensionality of the Brief Wisconsin Inventory of Smoking Dependence Motives and Identify Di…

2014

Introduction The present study aims to investigate the dimensionality of the brief version of the Wisconsin Inventory of Smoking Dependence Motives (B-WISDM) and identify different smoking motivational profiles among young daily smokers (N = 375). Methods We tested 3 measurement models of the B-WISDM using confirmatory factor analysis, whereas cluster analysis was used to identify the smokers' motivational profiles. Furthermore, we compared clusters toward dependence level and the number of cigarettes smoked per day using analysis of variance tests. Results The results confirmed that the B-WISDM measures 11 first-order intercorrelated factors. The second-order model, originally proposed for…

Dimension reduction: additional benefit of an optimal filter for independent component analysis to extract event-related potentials.

2011

The present study addresses benefits of a linear optimal filter (OF) for independent component analysis (ICA) in extracting brain event-related potentials (ERPs). A filter such as the digital filter is usually considered as a denoising tool. Actually, in filtering ERP recordings by an OF, the ERP' topography should not be changed by the filter, and the output should also be able to be modeled by the linear transformation. Moreover, an OF designed for a specific ERP source or component may remove noise, as well as reduce the overlap of sources and even reject some non-targeted sources in the ERP recordings. The OF can thus accomplish both the denoising and dimension reduction (reducing the n…

Self-Other Differentiation Scale: Dimensionality, IRT Parameterization, and Measurement Invariance

2018

The Self-Other Differentiation Scale (Olver, Aries, & Batgos, 1989) is a self-report instrument assessing the experience of a separate sense of self from others. The authors aimed to examine its dimensionality, reliability, and measurement invariance across gender. It was completed by 348 participants (48% men) from 17 to 30 years old in Study 1, 348 participants (40% men) from 18 to 28 years old in Study 2, and 1,068 participants (49% men) from 17 to 28 years old in Study 3. The results supported the hypothesis of just one factor underlying the scale; they also showed an appropriate internal consistency and a partial measurement invariance across gender. Results also showed evidence fo…

Earth system data cubes unravel global multivariate dynamics

2020

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cu…

Mixed Valence Materials: Prussian Blue Analogues of Reduced Dimensionality (Small 16/2012)

2012

Enthalpic and entropic contributions of water molecules to the functional T → R transition of human hemoglobin in solution

1992

Generalized solvent-mediated forces contribute to free energy at the functional T → R transition of human hemoglobin A (HbA). Their contribution is here sorted out quantitatively in both its enthalpic and entropic parts, along with the average number of water molecules involved. The latter (about 75 waters in average) must be considered together with HbA as one statistically defined functional unit for oxygen transport. Their configurations are expected to undergo frequent structural rearrangements. Lifetimes of statistically relevant configurations do not need to (although, of course, they may) exceed by more than a factor 5 the normal H-bond lifetimes of the pure solvent. Compared to the …

Comparison of Micro X-ray Computer Tomography Image Segmentation Methods: Artificial Neural Networks Versus Least Square Support Vector Machine

2013

Micro X-ray computer tomography (XCT) is a powerful non-destructive method for obtaining information about rock structures and mineralogy. A new methodology to obtain porosity from 2D XCT digital images using artificial neural network and least square support vector machine is demonstrated following these steps: the XCT image was first preprocessed, thereafter clustering algorithms such as K-means, Fuzzy c-means and self-organized maps was used for image segmentation. Then artificial neural network was applied for image classification. For comparison, least square support vector machine approach was used for classification labeling of the scan images. The methodology shows how artificial ne…

Regularized RBF Networks for Hyperspectral Data Classification

2004

In this paper, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dimensionality are tested for six images containing six crop classes. Also, regularization, sparseness, and knowledge extraction are paid attention.