Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Denoising using local projective subspace methods
Neurocomputing
Journal of VLSI Signal Processing Systems
Channel selection and feature projection for cognitive load estimation using ambulatory EEG
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
On Optimal Selection of Correlation Matrices for Matrix-Pencil-Based Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Blind separation of piecewise stationary non-Gaussian sources
Signal Processing
Heart and lung sound separation using periodic source extraction method
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
The generalized eigendecomposition approach to the blind source separation problem
Digital Signal Processing
Blind source separation based on self-organizing neural network
Engineering Applications of Artificial Intelligence
An eigenvector algorithm with reference signals using a deflation approach for blind deconvolution
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A learning framework for blind source separation using generalized eigenvalues
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Generic blind source separation using second-order local statistics
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Recursive generalized eigendecomposition for independent component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
A comparison of linear ICA and local linear ICA for mutual information based feature ranking
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Algebraic solutions to complex blind source separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Adaptive EEG artifact rejection for cognitive games
Proceedings of the 14th ACM international conference on Multimodal interaction
Blind Principles Based Interference and Noise Reduction Schemes for OFDM
Wireless Personal Communications: An International Journal
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In this short note we highlight the fact that linear blind source separation can be formulated as a generalized eigenvalue decomposition under the assumptions of non-Gaussian, non-stationary, or non-white independent sources. The solution for the unmixing matrix is given by the generalized eigenvectors that simultaneously diagonalize the covariance matrix of the observations and an additional symmetric matrix whose form depends upon the particular assumptions. The method critically determines the mixture coefficients and is therefore not robust to estimation errors. However it provides a rather general and unified solution that summarizes the conditions for successful blind source separation. To demonstrate the method, which can be implemented in two lines of matlab code, we present results for artificial mixtures of speech and real mixtures of electroencephalography (EEG) data, showing that the same sources are recovered under the various assumptions.