000145056 001__ 145056
000145056 005__ 20250926150152.0
000145056 0247_ $$2doi$$a10.1109/TBME.2024.3446806
000145056 0248_ $$2sideral$$a139901
000145056 037__ $$aART-2025-139901
000145056 041__ $$aeng
000145056 100__ $$aGrison, Agnese
000145056 245__ $$aA particle swarm optimised independence estimator for blind source separation of neurophysiological time series
000145056 260__ $$c2025
000145056 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145056 5203_ $$aThe 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.
000145056 536__ $$9info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101077693/EU/Extracting the Human Motor Null Space from Muscles - A new framework to measure human neural activity/ECHOES$$9info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031905-I
000145056 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000145056 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000145056 700__ $$aClarke, Alexander Kenneth
000145056 700__ $$aMuceli, Silvia
000145056 700__ $$0(orcid)0000-0001-8439-151X$$aIbáñez, Jaime$$uUniversidad de Zaragoza
000145056 700__ $$aKundu, Aritra
000145056 700__ $$aFarina, Dario
000145056 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000145056 773__ $$g72, 1 (2025), 227-237$$pIEEE trans. biomed. eng.$$tIEEE Transactions on Biomedical Engineering$$x0018-9294
000145056 8564_ $$s1587387$$uhttps://zaguan.unizar.es/record/145056/files/texto_completo.pdf$$yPostprint
000145056 8564_ $$s3473520$$uhttps://zaguan.unizar.es/record/145056/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000145056 909CO $$ooai:zaguan.unizar.es:145056$$particulos$$pdriver
000145056 951__ $$a2025-09-26-14:59:40
000145056 980__ $$aARTICLE