Search results for "Independent Component Analysis"

Deflation-Based FastICA With Adaptive Choices of Nonlinearities

2014

Deflation-based FastICA is a popular method for independent component analysis. In the standard deflation-base d approach the row vectors of the unmixing matrix are extracted one after another always using the same nonlinearities. In prac- tice the user has to choose the nonlinearities and the efficiency and robustness of the estimation procedure then strongly depends on this choice as well as on the order in which the components are extracted. In this paper we propose a novel adaptive two- stage deflation-based FastICA algorithm that (i) allows one to use different nonlinearities for different components and (ii) optimizes the order in which the components are extracted. Based on a consist…

Characterization of the interannual and intraseasonal variability of West African vegetation between 1982 and 2002 by means of NOAA AVHRR NDVI data

2007

AbstractThe interannual and intraseasonal variability of West African vegetation over the period 1982–2002 is studied using the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR).The novel independent component analysis (ICA) technique is applied to extract the main modes of the interannual variability of the vegetation, among which two modes are worth describing. The first component (IC1) describes NDVI variability over the Sahel from August to October. A strong photosynthetic activity over the Sahel is related to above-normal convection and rainfall within the intertropical convergence zone (ITCZ) in summertime and is partly associated …

Independent component analysis (ICA) in analysing child high-density event-related potential (ERP) and current source density (CSD) data

2008

2014

Due to its millisecond-scale temporal resolution, EEG allows to assess neural correlates with precisely defined temporal relationship relative to a given event. This knowledge is generally lacking in data from functional magnetic resonance imaging (fMRI) which has a temporal resolution on the scale of seconds so that possibilities to combine the two modalities are sought. Previous applications combining event-related potentials (ERPs) with simultaneous fMRI BOLD generally aimed at measuring known ERP components in single trials and correlate the resulting time series with the fMRI BOLD signal. While it is a valuable first step, this procedure cannot guarantee that variability of the chosen …

Spatial weighted averaging for ERP denoising in EEG data

2010

In the present paper we intend to improve the practical accuracy of ERP denoising methods proposed in earlier research by allowing them to take into account possible violations of the underlying assumptions, which often take place in practice. Here we consider ERP denoising approaches operating within the framework of the linear instantaneous mixing model that consist three steps: (1) forward linear transformation, (2) identification of the components related to signal and noise subspaces, (3) inverse transformation during which the components that belong to the noise subspace are disregarded, i.e. dimension reduction in the component space. The separation matrix is found based on problem-s…

A New Look at Spitzer Primary Transit Observations of the Exoplanet HD 189733b

2014

Blind source separation techniques are used to reanalyse two exoplanetary transit lightcurves of the exoplanet HD189733b recorded with the IR camera IRAC on board the Spitzer Space Telescope at 3.6$\mu$m during the "cold" era. These observations, together with observations at other IR wavelengths, are crucial to characterise the atmosphere of the planet HD189733b. Previous analyses of the same datasets reported discrepant results, hence the necessity of the reanalyses. The method we used here is based on the Independent Component Analysis (ICA) statistical technique, which ensures a high degree of objectivity. The use of ICA to detrend single photometric observations in a self-consistent wa…

Drawback of ICA Procedure on EEG: Polarity Indeterminacy at Local Optimization

2008

Independent component analysis (ICA) has been extensively applied to reject artifacts in electroencephalography (EEG) signal processing. The first step is to extract the independent component activations from the electrode records, and then project the desired components back to the electrodes. After the composition of the projected component is analyzed in details under ICA procedure, this study shows that since ICA may extract some source components at the local optimization in high-dimensional EEG signal space, the artificial polarity indeterminacy may happen on the projected component at some electrodes. By numerical simulations, this issue also exhibits that this polarity ambiguity occ…

Automated quality control protocol for MR spectra of brain tumors.

2008

Item does not contain fulltext eTUMOUR (http://www.etumour.net/) is acquiring a large database of brain tumor (1)H MR spectra to develop automated pattern recognition methods and decision support system (DSS) for tumor diagnosis. Development of accurate pattern-recognition algorithms requires spectra undistorted by artifacts, low signal-to-noise, or broad lines. eTUMOUR currently uses panels of expert spectroscopists to subjectively grade spectra as being acceptable or unacceptable. Automated quality control (QC) would be more satisfactory for several reasons: 1) to provide a reproducible objective classification of spectrum quality; 2) for use within the future DSS to prevent misdiagnosis …

Semi-blind Independent Component Analysis of functional MRI elicited by continuous listening to music

2013

This study presents a method to analyze blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (tMRI) signals associated with listening to continuous music. Semi-blind independent component analysis (ICA) was applied to decompose the tMRI data to source level activation maps and their respective temporal courses. The unmixing matrix in the source separation process of ICA was constrained by a variety of acoustic features derived from the piece of music used as the stimulus in the experiment. This allowed more stable estimation and extraction of more activation maps of interest compared to conventional ICA methods.

Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data

2020

In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because …