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The FastICA algorithm with spatial constraints
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DOI：10.1109/LSP.2005.856867.png)
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Abstract
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In many blind source separation (BSS) applications, especially for biomedical signal processing, there are specific expectations regarding the spatial and temporal characteristics of some sources, but post-hoc comparisons between source estimates and anticipated outcomes can be complicated and unreliable. One alternative is to incorporate additional prior knowledge, e.g., about the spatial topography of selected source sensor projections, into the BSS approach by means of constraints. This letter describes a modified version of the FastICA algorithm for spatially constrained BSS, where the estimates of selected columns of the mixing matrix are constrained with reference to predetermined source sensor projections.
Keywords:
biomedical signal processing
constrained independent component analysis (cICA)
FastICA
semi-blind source separation (SBSS)
spatial constraints
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9.6
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1.1W
Citations:
1.7W
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