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A Particle Swarm Optimized Independence Estimator for Blind Source Separation of Neurophysiological Time Series

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Title: A Particle Swarm Optimized Independence Estimator for Blind Source Separation of Neurophysiological Time Series
Authors: Grison, Agnese, Clarke, Alexander Kenneth, Muceli, Silvia, 1981, Ibáñez, Jaime, Kundu, Aritra, Farina, Dario
Source: IEEE Transactions on Biomedical Engineering. 72(1):227-237
Subject Terms: Independent component analysis, intramuscular electromyography, particle swarm optimisation, blind source separation, intracortical recording
Description: 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.
Access URL: https://research.chalmers.se/publication/545007
https://research.chalmers.se/publication/544972
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  Data: A Particle Swarm Optimized Independence Estimator for Blind Source Separation of Neurophysiological Time Series
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  Data: <searchLink fieldCode="AR" term="%22Grison%2C+Agnese%22">Grison, Agnese</searchLink><br /><searchLink fieldCode="AR" term="%22Clarke%2C+Alexander+Kenneth%22">Clarke, Alexander Kenneth</searchLink><br /><searchLink fieldCode="AR" term="%22Muceli%2C+Silvia%22">Muceli, Silvia</searchLink>, 1981<br /><searchLink fieldCode="AR" term="%22Ibáñez%2C+Jaime%22">Ibáñez, Jaime</searchLink><br /><searchLink fieldCode="AR" term="%22Kundu%2C+Aritra%22">Kundu, Aritra</searchLink><br /><searchLink fieldCode="AR" term="%22Farina%2C+Dario%22">Farina, Dario</searchLink>
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  Data: <i>IEEE Transactions on Biomedical Engineering</i>. 72(1):227-237
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  Data: <searchLink fieldCode="DE" term="%22Independent+component+analysis%22">Independent component analysis</searchLink><br /><searchLink fieldCode="DE" term="%22intramuscular+electromyography%22">intramuscular electromyography</searchLink><br /><searchLink fieldCode="DE" term="%22particle+swarm+optimisation%22">particle swarm optimisation</searchLink><br /><searchLink fieldCode="DE" term="%22blind+source+separation%22">blind source separation</searchLink><br /><searchLink fieldCode="DE" term="%22intracortical+recording%22">intracortical recording</searchLink>
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  Label: Description
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  Data: 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.
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      – Type: doi
        Value: 10.1109/TBME.2024.3446806
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 227
    Subjects:
      – SubjectFull: Independent component analysis
        Type: general
      – SubjectFull: intramuscular electromyography
        Type: general
      – SubjectFull: particle swarm optimisation
        Type: general
      – SubjectFull: blind source separation
        Type: general
      – SubjectFull: intracortical recording
        Type: general
    Titles:
      – TitleFull: A Particle Swarm Optimized Independence Estimator for Blind Source Separation of Neurophysiological Time Series
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            NameFull: Grison, Agnese
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            NameFull: Clarke, Alexander Kenneth
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            NameFull: Muceli, Silvia
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            NameFull: Ibáñez, Jaime
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            NameFull: Kundu, Aritra
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            NameFull: Farina, Dario
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            – D: 01
              M: 01
              Type: published
              Y: 2025
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