Blind Source Separation of Sparse Overcomplete Mixtures and Application to Neural Recordings

  • Authors:

  • Michal Natora;Felix Franke;Matthias Munk;Klaus Obermayer

  • Affiliations:

  • Institute for Software Engineering and Theoretical Computer Science, Berlin Institute of Technology, Germany;Bernstein Center for Computational Neuroscience, Berlin, Germany;Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Institute for Software Engineering and Theoretical Computer Science, Berlin Institute of Technology, Germany and Bernstein Center for Computational Neuroscience, Berlin, Germany

  • Venue:
  • ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
  • Year:
  • 2009

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Abstract

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.