Coherence Analysis of Surface Electromyography Signals of Facial Mimic Muscles

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dc.contributor.authorAlcan, Veysel
dc.contributor.authorSeker, Mehmet
dc.date.accessioned2025-03-17T12:25:38Z
dc.date.available2025-03-17T12:25:38Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024--AG289 Tarsus Univ Campus, Mersin, TURKEY
dc.description.abstractThis study aimed to perform a coherence analysis of surface electromyography (EMG) signals to investigate neural control mechanisms and coordination of facial expression muscles. A publicly available data set containing surface EMG data from the zygomaticus major (ZM), orbicularis oris (OO), and orbicularis oculi (OOc) muscles was used in the study. Experimental protocols included facial movements without gum chewing (condition A) and with chewing gum (condition B). Coherence analysis was used to understand the frequency-domain relationships between EMG signals. The results were compared statistically between the two conditions. Significant differences in coherence values were observed between conditions for the peak coherence value for ZM-OOc muscle pairs (p < 0.05). Coherence estimates for ZM-OOc showed a slight contribution at 10-60 Hz in condition A, while in condition B they showed a very significant contribution towards the 70-90 Hz band and reached a high value peak. This points to different neural control mechanisms and an increase in the synergetics of the muscles, especially with a mechanical effect such as chewing gum on the ZM-OOc muscle during various facial expressions. In conclusion, this study provided important information about neural control mechanisms and coordination patterns through the coherence analysis of surface EMG signals in facial expression muscles. The findings support targeted rehabilitation for motor disorders in facial muscles.
dc.description.sponsorshipIEEE,IEEE Turkey,Koluman & Berdan,Loodos,Figes,Turkcell,Yildirim Elect
dc.identifier.doi10.1109/SIU61531.2024.10600990
dc.identifier.isbn979-8-3503-8897-8
dc.identifier.isbn979-8-3503-8896-1
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85200908921
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10600990
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1794
dc.identifier.wosWOS:001297894700214
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIeee
dc.relation.ispartof32nd Ieee Signal Processing and Communications Applications Conference, Siu 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectsurface electromyography
dc.subjectfacial mimic muscles
dc.subjectcoherence analysis
dc.subjectmotor control
dc.subjectsynergistic muscles
dc.titleCoherence Analysis of Surface Electromyography Signals of Facial Mimic Muscles
dc.typeConference Object

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