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A Particle Swarm Optimised Independence Estimator for Blind Source Separation of Neurophysiological Time Series
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  • Agnese Grison ,
  • Alexander Kenneth Clarke ,
  • Silvia Muceli ,
  • Jaime Ibanez Pereda ,
  • Aritra Kundu ,
  • Dario Farina
Agnese Grison
Imperial College London

Corresponding Author:[email protected]

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Alexander Kenneth Clarke
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Silvia Muceli
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Jaime Ibanez Pereda
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Aritra Kundu
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Dario Farina
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The decomposition of neurophysiological recordings into their constituent neural sources is of major importance to a diverse range of neuroscientific fields and neuroengineering applications. The advent of high density electrode probes and arrays has driven a major need for novel semi-automated and automated blind source separation methodologies that take advantage of the increased spatial resolution and coverage these new devices offer. Independent component analysis (ICA) offers a principled theoretical framework for such algorithms, but implementation inefficiencies often drive poor performance in practice, particularly for sparse sources. Here we observe that the use of a single non-linear optimization function to identify spiking sources with ICA often has a detrimental effect that precludes the recovery and correct separation of all spiking sources in the signal. We go on to propose a projection-pursuit ICA algorithm designed specifically for spiking sources, which uses a particle swarm methodology to adaptively traverse a polynomial family of non-linearities approximating the asymmetric cumulants of the sources. We robustly prove state-of-the-art decomposition performance on recordings from high density intramuscular probes and demonstrate how the particle swarm quickly finds optimal contrast non-linearities across a range of neurophysiological datasets.