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- M. Natora, F. Franke,
M. Munk, and K. Obermayer. Blind Source Separation of Sparse Overcomplete
Mixtures and Application to Neural Recordings.
.
In Independent Component Analysis and Signal Separation, volume
5441 of Lecture Notes in Computer Science, pages 459-466, 2009.
(HTTP)
We present a method which allows for the blind source separation of
sparse overcomplete mixtures. In this method, linear filters are used to find
a new representation of the data and to enhance the signal-to-noise ratio.
Further, “Deconfusion”, a method similar to the independent component
analysis, decorrelates the filter outputs. In particular, the method was
developed to extract neural activity signals from extracellular recordings.
In this sense, the method can be viewed as a combined spike detection and
classification algorithm. We compare the performance of our method to those
of existing spike sorting algorithms, and also apply it to recordings from
real experiments with macaque monkeys.
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