A particle swarm optimised independence estimator for blind source separation of neurophysiological time series

Grison, Agnese ; Clarke, Alexander Kenneth ; Muceli, Silvia ; Ibáñez, Jaime (Universidad de Zaragoza) ; Kundu, Aritra ; Farina, Dario

Resumen: 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.

Idioma: Inglés

DOI: 10.1109/TBME.2024.3446806

Año: 2025

Publicado en: IEEE Transactions on Biomedical Engineering 72, 1 (2025), 227-237
ISSN: 0018-9294

Financiación: 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
Financiación: info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031905-I

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)