Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Independent components of magnetoencephalography: localization
Neural Computation
ICA with Sparse Connections: Revisited
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Bayesian independent component analysis with prior constraints: an application in biosignal analysis
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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We present new results about the simultaneous linear inverse problems using independent component analysis (ICA), which can be used to separate the data into statistically independent components. The idea of using ICA in solving such inverse problems, especially in EEG/MEG context, has been a known topic for at least more than a decade, but the known results have been justified heuristically, and their relationships are not understood properly. Here we show how to obtain a Bayesian posterior for a spatial source distribution, by using an ICA demixing matrix as an input. The posterior enables us to rederive and reinterpret the previously known methods, and also provides completely new methods.