Blind source separation using the maximum signal fraction approach

Authors:
Douglas R. Hundley;Michael J. Kirby;Markus Anderle
Affiliations:
Mathematics Department, Whitman College, Walla Walla, WA;Mathematics Department, Colorado State University, Fort Collins, CO;Mathematics Department, Colorado State University, Fort Collins, CO
Venue:
Signal Processing
Year:
2002

Citing 5
Cited 3

Oriented energy and oriented signal-to-signal ratio concepts in the analysis of vector sequences and time series

SVD and signal processing
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Blind Source Separation Using Temporal Predictability

Neural Computation
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks

Feature selection and blind source separation in an EEG-based brain-computer interface

EURASIP Journal on Applied Signal Processing
The generalized eigendecomposition approach to the blind source separation problem

Digital Signal Processing
Temporally local maximum signal fraction analysis for artifact removal from biomedical signals

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

It is shown that the independent components required to separate linearly mixed sources may be obtained by solving the generalized singular vector equation associated with a maximum signal fraction approach. This perspective permits the identification of a variational problem for blind source separation.