Examining Tongue Movement Intentions in EEG with Machine and Deep Learning: An Approach for Dysphagia Rehabilitation

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dc.contributor.authorAslan, Sevgi Gokce
dc.contributor.authorYilmaz, Bulent
dc.date.accessioned2026-04-04T13:32:56Z
dc.date.available2026-04-04T13:32:56Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description32nd European Signal Processing Conference (EUSIPCO) -- AUG 26-30, 2024 -- Lyon, FRANCE
dc.description.abstractDysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, Kernel, and Neural Network were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN and SVM, exhibited lower accuracy rates compared to ensemble methods like AdaBoost and Random Forest, which are effective in handling complex datasets. Additionally, a deep learning approach achieved an accuracy rate of 83%. Overall, this study demonstrates the promising role of BCI technologies and machine learning techniques in dysphagia rehabilitation.
dc.description.sponsorshipEuropean Assoc Signal Proc
dc.identifier.doi10.23919/EUSIPCO63174.2024.10715457
dc.identifier.endpage1391
dc.identifier.isbn978-9-4645-9361-7
dc.identifier.isbn979-8-3315-1977-3
dc.identifier.issn2076-1465
dc.identifier.scopus2-s2.0-85208426729
dc.identifier.scopusqualityN/A
dc.identifier.startpage1388
dc.identifier.urihttps://doi.org/10.23919/EUSIPCO63174.2024.10715457
dc.identifier.urihttps://hdl.handle.net/11616/108781
dc.identifier.wosWOS:001349787000278
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee
dc.relation.ispartof32nd European Signal Processing Conference, Eusipco 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectBCI
dc.subjectdysphagia
dc.subjectCNN
dc.subjectmachine learning
dc.subjectEEG
dc.subjectmotor imagery
dc.titleExamining Tongue Movement Intentions in EEG with Machine and Deep Learning: An Approach for Dysphagia Rehabilitation
dc.typeConference Object

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