Distinguishing Resting State From Motor Imagery Swallowing Using EEG and Deep Learning Models

Basit Öğe Kaydı
dc.contributor.authorAslan, Sevgi Gokce
dc.contributor.authorYilmaz, Bulent
dc.date.accessioned2026-04-04T13:33:24Z
dc.date.available2026-04-04T13:33:24Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description.abstractThe primary aim of this study was to assess the classification performance of deep learning models in distinguishing between resting state and motor imagery swallowing, utilizing various preprocessing and data visualization techniques applied to electroencephalography (EEG) data. In this study, we performed experiments using four distinct paradigms such as natural swallowing, induced saliva swallowing, induced water swallowing, and induced tongue protrusion on 30 right-handed individuals (aged 18 to 56). We utilized a 16-channel wearable EEG headset. We thoroughly investigated the impact of different preprocessing methods (Independent Component Analysis, Empirical Mode Decomposition, bandpass filtering) and visualization techniques (spectrograms, scalograms) on the classification performance of multichannel EEG signals. Additionally, we explored the utilization and potential contributions of deep learning models, particularly Convolutional Neural Networks (CNNs), in EEG-based classification processes. The novelty of this study lies in its comprehensive examination of the potential of deep learning models, specifically in distinguishing between resting state and motor imagery swallowing processes, using a diverse combination of EEG signal preprocessing and visualization techniques. The results showed that it was possible to distinguish the resting state from the imagination of swallowing with 89.8% accuracy, especially using continuous wavelet transform (CWT) based scalograms. The findings of this study may provide significant contributions to the development of effective methods for the rehabilitation and treatment of swallowing difficulties based on motor imagery-based brain computer interfaces.
dc.description.sponsorshipGulf University for Science and Technology, Kuwait
dc.description.sponsorshipThis work was supported by the Gulf University for Science and Technology, Kuwait, for covering the Article Processing Charges (APC).
dc.identifier.doi10.1109/ACCESS.2024.3501013
dc.identifier.endpage178389
dc.identifier.issn2169-3536
dc.identifier.orcid0000-0003-2954-1217
dc.identifier.orcid0000-0001-9425-1916
dc.identifier.scopus2-s2.0-85210293716
dc.identifier.scopusqualityQ1
dc.identifier.startpage178375
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3501013
dc.identifier.urihttps://hdl.handle.net/11616/109140
dc.identifier.volume12
dc.identifier.wosWOS:001373800700003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIEEE Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectElectroencephalography
dc.subjectMotors
dc.subjectTime-frequency analysis
dc.subjectFiltering
dc.subjectTongue
dc.subjectDeep learning
dc.subjectBrain modeling
dc.subjectSpectrogram
dc.subjectEmpirical mode decomposition
dc.subjectContinuous wavelet transforms
dc.subjectEEG
dc.subjectmotor imagery
dc.subjectscalogram
dc.subjectspectrogram
dc.subjectswallowing
dc.titleDistinguishing Resting State From Motor Imagery Swallowing Using EEG and Deep Learning Models
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu