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Handbook of Blind Source Separation: Independent Component Analysis and Applications
Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind SourceExpand
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Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture
Abstract The separation of independent sources from an array of sensors is a classical but difficult problem in signal processing. Based on some biological observations, an adaptive algorithm isExpand
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A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm
In this paper, a fast algorithm for overcomplete sparse decomposition, called SL0, is proposed. The algorithm is essentially a method for obtaining sparse solutions of underdetermined systems ofExpand
  • 827
  • 99
Multiclass Brain–Computer Interface Classification by Riemannian Geometry
This paper presents a new classification framework for brain-computer interface (BCI) based on motor imagery. This framework involves the concept of Riemannian geometry in the manifold of covarianceExpand
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Blind separation of sources
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Source separation in post-nonlinear mixtures
  • A. Taleb, C. Jutten
  • Mathematics, Computer Science
  • IEEE Trans. Signal Process.
  • 1 October 1999
We address the problem of separation of mutually independent sources in nonlinear mixtures. First, we propose theoretical results and prove that in the general case, it is not possible to separateExpand
  • 438
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OP-ELM: Optimally Pruned Extreme Learning Machine
In this brief, the optimally pruned extreme learning machine (OP-ELM) methodology is presented. It is based on the original extreme learning machine (ELM) algorithm with additional steps to make itExpand
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Space or time adaptive signal processing by neural network models
Part I. Starting from the properties of networks with backward lateral inhibitions, we define an algorithm for adaptive spatial sampling of line‐structured images. Applications to characterExpand
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A Nonlinear Bayesian Filtering Framework for ECG Denoising
In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based onExpand
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Classification of covariance matrices using a Riemannian-based kernel for BCI applications
The use of spatial covariance matrix as a feature is investigated for motor imagery EEG-based classification in brain-computer interface applications. A new kernel is derived by establishing aExpand
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