Temporal and spatial features of single-trial EEG for brain-computer interface
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Overcomplete topographic independent component analysis
Neurocomputing
Analysis of the Kurtosis-Sum Objective Function for ICA
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Perception of transformation-invariance in the visual pathway
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
An offline independent component analysis algorithm for colored sources
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Lp-Nested Symmetric Distributions
The Journal of Machine Learning Research
Self-adaptive FastICA based on generalized gaussian model
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Local independent factorization of natural scenes
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
An extended online Fast-ICA algorithm
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Local stability analysis of maximum nongaussianity estimation in independent component analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
ECG classification using ICA features and support vector machines
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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Independent component analysis is to extract independent signals from their linear mixtures without assuming prior knowledge of their mixing coefficients. As we know, a number of factors are likely to affect separation results in practical applications, such as the number of active sources, the distribution of source signals, and noise. The purpose of this paper to develop a general framework of blind separation from a practical point of view with special emphasis on the activation function adaptation. First, we propose the exponential generative model for probability density functions. A method of constructing an exponential generative model from the activation functions is discussed. Then, a learning algorithm is derived to update the parameters in the exponential generative model. The learning algorithm for the activation function adaptation is consistent with the one for training the demixing model. Stability analysis of the learning algorithm for the activation function is also discussed. Both theoretical analysis and simulations show that the proposed approach is universally convergent regardless of the distributions of sources. Finally, computer simulations are given to demonstrate the effectiveness and validity of the approach.