Adaptive blind separation with an unknown number of sources
Neural Computation
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Blind source separation with dynamic source number using adaptive neural algorithm
Expert Systems with Applications: An International Journal
Lead Field Space Projection for Spatiotemporal Imaging of Independent Brain Activities
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Fetal heart rate monitoring based on independent component analysis
Computers in Biology and Medicine
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Visualization of dynamic brain activities based on the single-trial MEG and EEG data analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Unified parametric and non-parametric ICA algorithm for arbitrary sources
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Analysis of the quasi-brain-death EEG data based on a robust ICA approach
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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We propose a robust approach for independent component analysis (ICA) of signals where observations are contaminated with high-level additive noise and/or outliers. The source signals may contain mixtures of both sub-Gaussian and super-Gaussian components, and the number of sources is unknown. Our robust approach includes two procedures. In the first procedure, a robust prewhitening technique is used to reduce the power of additive noise, the dimensionality and the correlation among sources. A cross-validation technique is introduced to estimate the number of sources in this first procedure. In the second procedure, a nonlinear function is derived using the parameterized t-distribution density model. This nonlinear function is robust against the undue influence of outliers fundamentally. Moreover, the stability of the proposed algorithm and the robust property of misestimating the parameters (kurtosis) have been studied. By combining the t-distribution model with a family of light-tailed distributions (sub-Gaussian) model, we can separate the mixture of sub-Gaussian and super-Gaussian source components. Through the analysis of artificially synthesized data and real-world magnetoencephalographic (MEG) data, we illustrate the efficacy of this robust approach.