A principal component entropy metric for assessing global synchronicity in EEG signals

Autores
Diambra, Luis Anibal; Hutber, Anna; Drakeford Hafeez, Zakarriah; Mi, Ran; Tsirka, Vasiliki; Capurro, Alberto
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Neuronal oscillations and their inter-areal synchronisation are fundamental for brain function and cognitive processes. While electrophysiological recordings, such as electroencephalography (EEG), provide invaluable insights, existing quantitative methodologies for assessing neuronal synchrony in EEG often focus on pairwise interactions, thereby limiting a comprehensive understanding of global network coordination. This study proposes principal component (PC)-entropy, a novel multichannel synchronisation metric designed to quantify the global degree of synchrony within brain signals. PC-entropy is a hybrid measure derived from Principal Component Analysis and Shannon entropy, specifically by applying normalised entropy to the eigenvalues obtained from data covariance. This approach effectively translates the distribution of variance across principal components into a synchrony measure, ranging from 0 (perfect synchrony) to 1 (complete desynchronisation), and is notably robust to variations in the number of recording channels. We validated PC-entropy using synthetic data from the Kuramoto model, including non-isofrequency signals, demonstrating its efficacy in assessing synchronisation. PC-entropy was then applied to three human EEG datasets, demonstrating its utility in detecting neural synchrony changes during sleep, differentiating nocturnal frontal lobe epilepsy (NFLE) patients from controls, reflecting consciousness levels in coma patients, and distinguishing arithmetic task performance. PC-entropy offers a valuable and sensitive tool for assessing global brain synchrony. It provides a new dimension for understanding functional connectivity and various physiological states, extending beyond the limitations of pairwise analyses and conventional spectral approaches.
Fil: Diambra, Luis Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Centro de Endocrinología Experimental y Aplicada; Argentina
Fil: Hutber, Anna. Queen Mary University; Reino Unido
Fil: Drakeford Hafeez, Zakarriah. Queen Mary University; Reino Unido
Fil: Mi, Ran. Queen Mary University; Reino Unido
Fil: Tsirka, Vasiliki. Queen Mary University; Reino Unido
Fil: Capurro, Alberto. Queen Mary University; Reino Unido
Materia
SLEEP
SYNCHRONICITY
EEG
NFLE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/288389
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A principal component entropy metric for assessing global synchronicity in EEG signalsDiambra, Luis AnibalHutber, AnnaDrakeford Hafeez, ZakarriahMi, RanTsirka, VasilikiCapurro, AlbertoSLEEPSYNCHRONICITYEEGNFLEhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1https://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Neuronal oscillations and their inter-areal synchronisation are fundamental for brain function and cognitive processes. While electrophysiological recordings, such as electroencephalography (EEG), provide invaluable insights, existing quantitative methodologies for assessing neuronal synchrony in EEG often focus on pairwise interactions, thereby limiting a comprehensive understanding of global network coordination. This study proposes principal component (PC)-entropy, a novel multichannel synchronisation metric designed to quantify the global degree of synchrony within brain signals. PC-entropy is a hybrid measure derived from Principal Component Analysis and Shannon entropy, specifically by applying normalised entropy to the eigenvalues obtained from data covariance. This approach effectively translates the distribution of variance across principal components into a synchrony measure, ranging from 0 (perfect synchrony) to 1 (complete desynchronisation), and is notably robust to variations in the number of recording channels. We validated PC-entropy using synthetic data from the Kuramoto model, including non-isofrequency signals, demonstrating its efficacy in assessing synchronisation. PC-entropy was then applied to three human EEG datasets, demonstrating its utility in detecting neural synchrony changes during sleep, differentiating nocturnal frontal lobe epilepsy (NFLE) patients from controls, reflecting consciousness levels in coma patients, and distinguishing arithmetic task performance. PC-entropy offers a valuable and sensitive tool for assessing global brain synchrony. It provides a new dimension for understanding functional connectivity and various physiological states, extending beyond the limitations of pairwise analyses and conventional spectral approaches.Fil: Diambra, Luis Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Hutber, Anna. Queen Mary University; Reino UnidoFil: Drakeford Hafeez, Zakarriah. Queen Mary University; Reino UnidoFil: Mi, Ran. Queen Mary University; Reino UnidoFil: Tsirka, Vasiliki. Queen Mary University; Reino UnidoFil: Capurro, Alberto. Queen Mary University; Reino UnidoNature2026-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/288389Diambra, Luis Anibal; Hutber, Anna; Drakeford Hafeez, Zakarriah; Mi, Ran; Tsirka, Vasiliki; et al.; A principal component entropy metric for assessing global synchronicity in EEG signals; Nature; Scientific Reports; 16; 1; 3-2026; 1-122045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-026-36434-0info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-026-36434-0info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-06-17T09:42:04Zoai:ri.conicet.gov.ar:11336/288389instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-06-17 09:42:04.854CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A principal component entropy metric for assessing global synchronicity in EEG signals
title A principal component entropy metric for assessing global synchronicity in EEG signals
spellingShingle A principal component entropy metric for assessing global synchronicity in EEG signals
Diambra, Luis Anibal
SLEEP
SYNCHRONICITY
EEG
NFLE
title_short A principal component entropy metric for assessing global synchronicity in EEG signals
title_full A principal component entropy metric for assessing global synchronicity in EEG signals
title_fullStr A principal component entropy metric for assessing global synchronicity in EEG signals
title_full_unstemmed A principal component entropy metric for assessing global synchronicity in EEG signals
title_sort A principal component entropy metric for assessing global synchronicity in EEG signals
dc.creator.none.fl_str_mv Diambra, Luis Anibal
Hutber, Anna
Drakeford Hafeez, Zakarriah
Mi, Ran
Tsirka, Vasiliki
Capurro, Alberto
author Diambra, Luis Anibal
author_facet Diambra, Luis Anibal
Hutber, Anna
Drakeford Hafeez, Zakarriah
Mi, Ran
Tsirka, Vasiliki
Capurro, Alberto
author_role author
author2 Hutber, Anna
Drakeford Hafeez, Zakarriah
Mi, Ran
Tsirka, Vasiliki
Capurro, Alberto
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv SLEEP
SYNCHRONICITY
EEG
NFLE
topic SLEEP
SYNCHRONICITY
EEG
NFLE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Neuronal oscillations and their inter-areal synchronisation are fundamental for brain function and cognitive processes. While electrophysiological recordings, such as electroencephalography (EEG), provide invaluable insights, existing quantitative methodologies for assessing neuronal synchrony in EEG often focus on pairwise interactions, thereby limiting a comprehensive understanding of global network coordination. This study proposes principal component (PC)-entropy, a novel multichannel synchronisation metric designed to quantify the global degree of synchrony within brain signals. PC-entropy is a hybrid measure derived from Principal Component Analysis and Shannon entropy, specifically by applying normalised entropy to the eigenvalues obtained from data covariance. This approach effectively translates the distribution of variance across principal components into a synchrony measure, ranging from 0 (perfect synchrony) to 1 (complete desynchronisation), and is notably robust to variations in the number of recording channels. We validated PC-entropy using synthetic data from the Kuramoto model, including non-isofrequency signals, demonstrating its efficacy in assessing synchronisation. PC-entropy was then applied to three human EEG datasets, demonstrating its utility in detecting neural synchrony changes during sleep, differentiating nocturnal frontal lobe epilepsy (NFLE) patients from controls, reflecting consciousness levels in coma patients, and distinguishing arithmetic task performance. PC-entropy offers a valuable and sensitive tool for assessing global brain synchrony. It provides a new dimension for understanding functional connectivity and various physiological states, extending beyond the limitations of pairwise analyses and conventional spectral approaches.
Fil: Diambra, Luis Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Centro de Endocrinología Experimental y Aplicada; Argentina
Fil: Hutber, Anna. Queen Mary University; Reino Unido
Fil: Drakeford Hafeez, Zakarriah. Queen Mary University; Reino Unido
Fil: Mi, Ran. Queen Mary University; Reino Unido
Fil: Tsirka, Vasiliki. Queen Mary University; Reino Unido
Fil: Capurro, Alberto. Queen Mary University; Reino Unido
description Neuronal oscillations and their inter-areal synchronisation are fundamental for brain function and cognitive processes. While electrophysiological recordings, such as electroencephalography (EEG), provide invaluable insights, existing quantitative methodologies for assessing neuronal synchrony in EEG often focus on pairwise interactions, thereby limiting a comprehensive understanding of global network coordination. This study proposes principal component (PC)-entropy, a novel multichannel synchronisation metric designed to quantify the global degree of synchrony within brain signals. PC-entropy is a hybrid measure derived from Principal Component Analysis and Shannon entropy, specifically by applying normalised entropy to the eigenvalues obtained from data covariance. This approach effectively translates the distribution of variance across principal components into a synchrony measure, ranging from 0 (perfect synchrony) to 1 (complete desynchronisation), and is notably robust to variations in the number of recording channels. We validated PC-entropy using synthetic data from the Kuramoto model, including non-isofrequency signals, demonstrating its efficacy in assessing synchronisation. PC-entropy was then applied to three human EEG datasets, demonstrating its utility in detecting neural synchrony changes during sleep, differentiating nocturnal frontal lobe epilepsy (NFLE) patients from controls, reflecting consciousness levels in coma patients, and distinguishing arithmetic task performance. PC-entropy offers a valuable and sensitive tool for assessing global brain synchrony. It provides a new dimension for understanding functional connectivity and various physiological states, extending beyond the limitations of pairwise analyses and conventional spectral approaches.
publishDate 2026
dc.date.none.fl_str_mv 2026-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/288389
Diambra, Luis Anibal; Hutber, Anna; Drakeford Hafeez, Zakarriah; Mi, Ran; Tsirka, Vasiliki; et al.; A principal component entropy metric for assessing global synchronicity in EEG signals; Nature; Scientific Reports; 16; 1; 3-2026; 1-12
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/288389
identifier_str_mv Diambra, Luis Anibal; Hutber, Anna; Drakeford Hafeez, Zakarriah; Mi, Ran; Tsirka, Vasiliki; et al.; A principal component entropy metric for assessing global synchronicity in EEG signals; Nature; Scientific Reports; 16; 1; 3-2026; 1-12
2045-2322
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-026-36434-0
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-026-36434-0
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature
publisher.none.fl_str_mv Nature
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.040872

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