Understanding the relationship between rosemary odor and mental workload through deep learning

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dc.contributor.authorŞahin Sadık, Evin
dc.contributor.authorSaraoğlu, Hamdi Melih
dc.contributor.authorCanbaz Kabay, Sibel
dc.contributor.authorKeskinkılıç, Cahit
dc.date.accessioned2025-10-30T07:01:41Z
dc.date.available2025-10-30T07:01:41Z
dc.date.issued2025
dc.departmentFakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Psikoloji Bölümü
dc.description.abstractThis research explores the novel application of aromatic odors, specifically rosemary, in reducing mental workload, employing deep learning methods to analyze electroencephalogram (EEG) signals without feature extraction. Thirty volunteers participated in five neuropsychological tests while being exposed to the aroma of rosemary. The EEG signals recorded during the performance of these tasks were analyzed using deep learning methods to classify mental workload. Deep learning algorithms such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) were employed to classify mental workload directly from EEG signals. The analysis revealed that volunteers exposed to the rosemary odor showed decreased error rates and increased test success and learning scores, in comparison to a condition without odor. The classification of mental workload under rosemary odor exposure was achieved with a high accuracy rate of 97.11% in both deep learning algorithms. This study presents a novel approach by combining olfactory stimulation and EEG-based mental workload classification through deep learning. These findings suggest that rosemary odor may reduce mental workload and that raw EEG signals can be effectively analyzed using deep learning without manual feature engineering.
dc.description.sponsorshipKütahya Dumlupınar University
dc.identifier.doi10.1016/j.neuroscience.2025.09.038
dc.identifier.endpage83
dc.identifier.issn0306-4522
dc.identifier.pmid41072604
dc.identifier.scopus2-s2.0-105018932885
dc.identifier.scopusqualityQ1
dc.identifier.startpage72
dc.identifier.urihttps://doi.org/10.1016/j.neuroscience.2025.09.038
dc.identifier.urihttps://hdl.handle.net/11501/2493
dc.identifier.volume588
dc.identifier.wosWOS:001600561100008
dc.identifier.wosqualityQ3
dc.indekslendigikaynakPubMed
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorKeskinkılıç, Cahit
dc.institutionauthorid0000-0003-3799-4427
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofNeuroscience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClassification
dc.subjectDeep Learning
dc.subjectEEG
dc.subjectMental Workload
dc.subjectNeuropsychological Task
dc.titleUnderstanding the relationship between rosemary odor and mental workload through deep learning
dc.typeArticle

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