A neural net for blind separation of nonstationary signals
Neural Networks
Blind source separation for convolutive mixtures
Signal Processing
Independent component analysis: algorithms and applications
Neural Networks
A fast algorithm for one-unit ICA-R
Information Sciences: an International Journal
Artificial Intelligence in Medicine
On the convergence of ICA algorithms with symmetric orthogonalization
IEEE Transactions on Signal Processing
An improved method for independent component analysis with reference
Digital Signal Processing
IEEE Transactions on Signal Processing
Independent component analysis by entropy bound minimization
IEEE Transactions on Signal Processing
Content-based facial image retrieval using constrained independent component analysis
Information Sciences: an International Journal
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
A matrix-pencil approach to blind separation of colorednonstationary signals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Approach and applications of constrained ICA
IEEE Transactions on Neural Networks
A New Constrained Independent Component Analysis Method
IEEE Transactions on Neural Networks
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When prior knowledge is available, it is beneficial to use constrained blind source separation (BSS) algorithms that can utilize more information to distinguish the desired source from artifacts and noise. This paper proposes a one-unit second-order blind identification with reference (SOBI-R) algorithm for short transient signal extraction, which reformulates the conventional second-order blind identification (SOBI) algorithm in an iterative manner to achieve joint diagonalization and the reference information incorporated. The proposed algorithm was applied to single trial extraction of somatosensory evoked potential (SEP). The experimental results demonstrated its effectiveness. Compared with other algorithms including the autoregressive model with exogenous input (ARX), artificial neural networks (ANN) and one-unit the independent component analysis with reference (ICA-R), the proposed SOBI-R algorithm shows high robustness under conditions with low signal-to-noise ratios and less sensitivity to the reference signal.