A Particle Swarm Optimized Independence Estimator for Blind Source Separation of Neurophysiological Time Series
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| Název: | A Particle Swarm Optimized Independence Estimator for Blind Source Separation of Neurophysiological Time Series |
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| Autoři: | Grison, Agnese, Clarke, Alexander Kenneth, Muceli, Silvia, 1981, Ibáñez, Jaime, Kundu, Aritra, Farina, Dario |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 72(1):227-237 |
| Témata: | Independent component analysis, intramuscular electromyography, particle swarm optimisation, blind source separation, intracortical recording |
| Popis: | 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. |
| Přístupová URL adresa: | https://research.chalmers.se/publication/545007 https://research.chalmers.se/publication/544972 |
| Databáze: | SwePub |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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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| ISSN: | 00189294 15582531 |
| DOI: | 10.1109/TBME.2024.3446806 |