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INDEPENDENT COMPONENT DISCRIMINANT ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION
"... In this paper, the use of Independent Component Discriminant Anal-ysis (ICDA) for remote sensing classification is proposed. ICDA is a non-parametric method for discriminant analysis based on the ap-plication of a Bayesian classification rule on a signal composed by independent components. The metho ..."
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In this paper, the use of Independent Component Discriminant Anal-ysis (ICDA) for remote sensing classification is proposed. ICDA is a non-parametric method for discriminant analysis based on the ap-plication of a Bayesian classification rule on a signal composed by independent components
Hyperspectral image classification with independent component discriminant analysis
- IEEE Trans. Geosci. Remote Sens
, 2011
"... Abstract—In this paper, the use of Independent Component (IC) Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a nonparametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by ICs. The method uses I ..."
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Cited by 4 (1 self)
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Abstract—In this paper, the use of Independent Component (IC) Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a nonparametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by ICs. The method uses
controls using independent ERP components and a support vector machine: a validation study
"... Background: There are numerous event-related potential (ERP) studies in relation to attention-deficit hyperactivity disorder (ADHD), and a substantial number of ERP correlates of the disorder have been identified. However, most of the studies are limited to group differences in children. Independent ..."
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. Independent component analysis (ICA) separates a set of mixed event-related potentials into a corresponding set of statistically independent source signals, which are likely to represent different functional processes. Using a support vector machine (SVM), a classification method originating from machine
Classification of Artifactual ICA Components
"... Abstract. The analysis of EEG signals for the use in BCI systems and for mental state monitoring applications is often impeded by artifacts caused by muscular activity or external technical sources. A promising approach for the reduction or removal of artifacts is based on methods of Blind Source Se ..."
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Separation (BSS), which transform the original EEG signal into independent source components. In order to avoid the time-consuming hand rating of sources into artifactual and non-artifactual components, an automated method for their classification is proposed. Applying state of the art machine learning
Classification of Artifactual ICA Components
"... The analysis of EEG signals for the use in BCI systems and for mental state monitoring applications is often impeded by artifacts caused by muscular activity or external technical sources. A promising approach for the reduction or removal of artifacts is based on methods of Blind Source Separation ..."
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(BSS), which transform the original EEG signal into independent source components. In order to avoid the time-consuming hand rating of sources into artifactual and non-artifactual components, an automated method for their classification is proposed. Applying state of the art machine learning algorithms
Classification of Artifactual ICA Components
"... Abstract. The analysis of EEG signals for the use in BCI systems and for mental state monitoring applications is often impeded by artifacts caused by muscular activity or external technical sources. A promising approach for the reduction or removal of artifacts is based on methods of Blind Source Se ..."
Abstract
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Separation (BSS), which transform the original EEG signal into independent source components. In order to avoid the time-consuming hand rating of sources into artifactual and non-artifactual components, an automated method for their classification is proposed. Applying state of the art machine learning
Recognition of motor imagery electroencephalography using independent component analysis and machine classifiers
- In International Conference on Computer Graphics, Visualization and Computer Vision
, 2004
"... Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulation of left or right finger lifting tasks, can be used as neural input signals to activate brain computer interface (BCI). The effectiveness of such an EEG-based BCI system relies on two indispensable ..."
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Cited by 8 (0 self)
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indispensable features: distinguishable patterns of brain signals and accurate classifiers. This work aims to extract a reliable neural feature, termed as beta rebound map, out of motor imagery EEG by means of independent component analysis, and employ four classifiers to investigate the efficacy of beta
Recognition of Motor Imagery Electroencephalography Using Independent Component Analysis and Machine Classifiers
, 2005
"... Abstract—Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulation of left or right finger lifting tasks, can be used to provide neural input signals to activate a brain computer interface (BCI). The effectiveness of such an EEG-based BCI system relies o ..."
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on two indispensable com-ponents: distinguishable patterns of brain signals and accurate classifiers. This work aims to extract two reliable neural features, termed contralateral and ipsilateral rebound maps, by removing artifacts from motor imagery EEG based on independent compo-nent analysis (ICA
doi:10.1155/2007/82069 Research Article Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation
"... We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, t ..."
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We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method
Linear Discriminant Analysis For Signal Processing Problems
, 1999
"... Linear Discriminant Analysis (LDA) and Principal Components Analysis (PCA) are two common techniques used for classification and dimensionality reduction. These techniques typically use a linear transformation which can either be implemented in a class-dependent or class-independent fashion. PCA is ..."
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along with two other classification techniques, Support Vector Machines (SVM) and Independent Components Analysis (ICA). The problem of classifying forestry images based on their scenic beauty is considered. On a standard evaluation task consisting of 478 training images and 159 test images, class
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