On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features

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dc.contributor.authorOmejc, Nina
dc.contributor.authorPeskar, Manca
dc.contributor.authorMiladinović, Aleksandar
dc.contributor.authorKavcic, Voyko
dc.contributor.authorDžeroski, Sašo
dc.contributor.authorMarusic, Uros
dc.date.accessioned2023-02-08T13:07:17Z
dc.date.available2023-02-08T13:07:17Z
dc.date.issued2023-01-31
dc.date.updated2023-02-03T17:14:50Z
dc.description.abstractThe utilization of a non-invasive electroencephalogram (EEG) as an input sensor is a common approach in the field of the brain–computer interfaces (BCI). However, the collected EEG data pose many challenges, one of which may be the age-related variability of event-related potentials (ERPs), which are often used as primary EEG BCI signal features. To assess the potential effects of aging, a sample of 27 young and 43 older healthy individuals participated in a visual oddball study, in which they passively viewed frequent stimuli among randomly occurring rare stimuli while being recorded with a 32-channel EEG set. Two types of EEG datasets were created to train the classifiers, one consisting of amplitude and spectral features in time and another with extracted time-independent statistical ERP features. Among the nine classifiers tested, linear classifiers performed best. Furthermore, we show that classification performance differs between dataset types. When temporal features were used, maximum individuals’ performance scores were higher, had lower variance, and were less affected overall by within-class differences such as age. Finally, we found that the effect of aging on classification performance depends on the classifier and its internal feature ranking. Accordingly, performance will differ if the model favors features with large within-class differences. With this in mind, care must be taken in feature extraction and selection to find the correct features and consequently avoid potential age-related performance degradation in practice.
dc.description.sponsorshipEC/H2020/952401/EU/TWINning the BRAIN with machine learning for neuro-muscular efficiency/TwinBrain
dc.identifier.eissn2075-1729
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18167
dc.identifier.urihttps://doi.org/10.14279/depositonce-16960
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc570 Biowissenschaften; Biologiede
dc.subject.otheraging
dc.subject.otherEEG
dc.subject.othermachine learning
dc.subject.otherclassification
dc.subject.otherBCI
dc.subject.othervisual oddball study
dc.titleOn the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber391
dcterms.bibliographicCitation.doi10.3390/life13020391
dcterms.bibliographicCitation.issue2
dcterms.bibliographicCitation.journaltitleLife
dcterms.bibliographicCitation.originalpublishernameMDPI
dcterms.bibliographicCitation.originalpublisherplaceBasel
dcterms.bibliographicCitation.volume13
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 5 Verkehrs- und Maschinensysteme::Inst. Psychologie und Arbeitswissenschaft::FG Biopsychologie und Neuroergonomie
tub.publisher.universityorinstitutionTechnische Universität Berlin

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