145056 20250926150152.0 doi 10.1109/TBME.2024.3446806 sideral 139901 ART-2025-139901 eng Grison, Agnese A particle swarm optimised independence estimator for blind source separation of neurophysiological time series 2025 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101077693/EU/Extracting the Human Motor Null Space from Muscles - A new framework to measure human neural activity/ECHOES info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031905-I info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Clarke, Alexander Kenneth Muceli, Silvia Ibáñez, Jaime Universidad de Zaragoza (orcid)0000-0001-8439-151X Kundu, Aritra Farina, Dario 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 72, 1 (2025), 227-237 IEEE trans. biomed. eng. IEEE Transactions on Biomedical Engineering 0018-9294 1587387 http://zaguan.unizar.es/record/145056/files/texto_completo.pdf Postprint 3473520 http://zaguan.unizar.es/record/145056/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:145056 articulos driver 2025-09-26-14:59:40 ARTICLE