Blind separation methods based on Pearson system and its extensions
Signal Processing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Extraction of Specific Signals with Temporal Structure
Neural Computation
A two-stage based approach for extracting periodic signals
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Input feature selection for classification problems
IEEE Transactions on Neural Networks
A robust approach to independent component analysis of signals with high-level noise measurements
IEEE Transactions on Neural Networks
Approach and applications of constrained ICA
IEEE Transactions on Neural Networks
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To extract source signals with certain temporal structures, such as periodicity, we propose a two-stage extraction algorithm. Its first stage uses the autocorrelation property of the desired source signal, and the second stage exploits the independence assumption. The algorithm is suitable to extract periodic or quasi-periodic source signals, without requiring that they have distinct periods. It outperforms many existing algorithms in many aspects, confirmed by simulations. Finally, we use the proposed algorithm to extract the components of visual event-related potentials evoked by three geometrical figure stimuli, and the classification accuracy based on the extracted components achieves 93.2%.