Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
A new concept for separability problems in blind source separation
Neural Computation
IEEE Transactions on Signal Processing
Pattern Recognition Letters
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis
Information Sciences: an International Journal
Fast PCA for processing calcium-imaging data from the brain of drosophila melanogaster
Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics
Brain connectivity analysis: a short survey
Computational Intelligence and Neuroscience
Functional activity maps based on significance measures and Independent Component Analysis
Computer Methods and Programs in Biomedicine
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Data sets acquired from functional magnetic resonance imaging (fMRI) contain both spatial and temporal structures. In order to blindly extract underlying activities, the common approach however only uses either spatial or temporal independence. More convincing results can be achieved by requiring the transformed data to be as independent as possible in both domains. First introduced by Stone, spatiotemporal independent component analysis (ICA) is a promising algorithm for fMRI decomposition. We propose an algebraic spatiotemporal ICA algorithm with increased performance and robustness. The feasibility of the algorithm is demonstrated in an application to the analysis of an fMRI data sets of a human brain performing an auditory task.