Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task

Research on the functioning of human cognition has been a crucial problem studied for years. Electroencephalography (EEG) classification methods may serve as a precious tool for understanding the temporal dynamics of human brain activity, and the purpose of such an approach is to increase the statis...

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Autores principales:	Karina Maciejewska, Wojciech Froelich
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	gender identification event related potentials ERP signal classification data mining Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/f75872ccd3a5452784cd55f38993f81e
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spelling	oai:doaj.org-article:f75872ccd3a5452784cd55f38993f81e2021-11-25T17:30:53ZHierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task10.3390/e231115471099-4300https://doaj.org/article/f75872ccd3a5452784cd55f38993f81e2021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1547https://doaj.org/toc/1099-4300Research on the functioning of human cognition has been a crucial problem studied for years. Electroencephalography (EEG) classification methods may serve as a precious tool for understanding the temporal dynamics of human brain activity, and the purpose of such an approach is to increase the statistical power of the differences between conditions that are too weak to be detected using standard EEG methods. Following that line of research, in this paper, we focus on recognizing gender differences in the functioning of the human brain in the attention task. For that purpose, we gathered, analyzed, and finally classified event-related potentials (ERPs). We propose a hierarchical approach, in which the electrophysiological signal preprocessing is combined with the classification method, enriched with a segmentation step, which creates a full line of electrophysiological signal classification during an attention task. This approach allowed us to detect differences between men and women in the P3 waveform, an ERP component related to attention, which were not observed using standard ERP analysis. The results provide evidence for the high effectiveness of the proposed method, which outperformed a traditional statistical analysis approach. This is a step towards understanding neuronal differences between men’s and women’s brains during cognition, aiming to reduce the misdiagnosis and adverse side effects in underrepresented women groups in health and biomedical research.Karina MaciejewskaWojciech FroelichMDPI AGarticlegender identificationevent related potentialsERP signal classificationdata miningScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1547, p 1547 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gender identification event related potentials ERP signal classification data mining Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	gender identification event related potentials ERP signal classification data mining Science Q Astrophysics QB460-466 Physics QC1-999 Karina Maciejewska Wojciech Froelich Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task
description	Research on the functioning of human cognition has been a crucial problem studied for years. Electroencephalography (EEG) classification methods may serve as a precious tool for understanding the temporal dynamics of human brain activity, and the purpose of such an approach is to increase the statistical power of the differences between conditions that are too weak to be detected using standard EEG methods. Following that line of research, in this paper, we focus on recognizing gender differences in the functioning of the human brain in the attention task. For that purpose, we gathered, analyzed, and finally classified event-related potentials (ERPs). We propose a hierarchical approach, in which the electrophysiological signal preprocessing is combined with the classification method, enriched with a segmentation step, which creates a full line of electrophysiological signal classification during an attention task. This approach allowed us to detect differences between men and women in the P3 waveform, an ERP component related to attention, which were not observed using standard ERP analysis. The results provide evidence for the high effectiveness of the proposed method, which outperformed a traditional statistical analysis approach. This is a step towards understanding neuronal differences between men’s and women’s brains during cognition, aiming to reduce the misdiagnosis and adverse side effects in underrepresented women groups in health and biomedical research.
format	article
author	Karina Maciejewska Wojciech Froelich
author_facet	Karina Maciejewska Wojciech Froelich
author_sort	Karina Maciejewska
title	Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task
title_short	Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task
title_full	Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task
title_fullStr	Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task
title_full_unstemmed	Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task
title_sort	hierarchical classification of event-related potentials for the recognition of gender differences in the attention task
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f75872ccd3a5452784cd55f38993f81e
work_keys_str_mv	AT karinamaciejewska hierarchicalclassificationofeventrelatedpotentialsfortherecognitionofgenderdifferencesintheattentiontask AT wojciechfroelich hierarchicalclassificationofeventrelatedpotentialsfortherecognitionofgenderdifferencesintheattentiontask
_version_	1718412262611353600

Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task

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