Migraine Analysis with Cross Entropy Based Connectivity Feature: Investigation of Sensory Stimulus Conditions

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dc.contributor.authorOrhanbulucu, Firat
dc.contributor.authorLatifoglu, Fatma
dc.date.accessioned2026-04-04T13:33:24Z
dc.date.available2026-04-04T13:33:24Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications-ICHORA -- MAY 23-24, 2025 -- Ankara, TURKIYE
dc.description.abstractMigraine is one of the most common neurological disorders. Despite its high prevalence, the lack of research on migraine compared to other neurological disorders is striking. Visual or auditory sensory stimuli are effective in triggering migraine attacks. The relationships between different brain regions depending on the stimuli can be analyzed with brain connectivity properties by creating a connectivity matrix. In this study, the effect of sensory stimuli in migraine patients is analyzed by entropy-based connectivity analysis and machine learning algorithms Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. At the same time, the classification process was carried out with a healthy control group according to the stimulus conditions in order to contribute to the development of systems that can help diagnose migraine. Unlike previous studies, calculations were made between electrode pairs by applying permutation and conditional Cross Entropy (CE) techniques to Electroencephalography (EEG) signals and connectivity feature matrix or map showing the connection between electrodes was created. In the analysis of the stimulus effect in migraine patients, the most successful classification was obtained between resting and auditory stimulus conditions in the ANN algorithm (Accuracy: 83.68%). In the classification of migraine patients with healthy control group, the ANN algorithm and auditory stimulus condition gave the most successful accuracy rate (Accuracy: 85.71%). According to the analyses in this study, it was determined that the auditory stimulus condition may show significant differences in certain channels in certain regions of the brain of migraine patients compared to visual stimulus and resting conditions and healthy participants. The proposed approach (Fused CE+ANN) has shown successful performance in analyzing the stimulus effect and predicting migraine in migraine patients. Cross entropy techniques can help to discover brain connectivity features that may occur in the brain activity information that may occur especially under stimulus effect.
dc.description.sponsorshipErciyes University Scientific Research Projects Unit [FDK-2023-13425]
dc.description.sponsorshipErciyes University Scientific Research Projects Unit contributed to this study with the project number FDK-2023-13425.
dc.description.sponsorshipInstitute of Electrical and Electronics Engineers Inc,Ted University
dc.identifier.doi10.1109/ICHORA65333.2025.11017155
dc.identifier.isbn979-8-3315-1089-3
dc.identifier.isbn979-8-3315-1088-6
dc.identifier.issn2996-4385
dc.identifier.orcid0000-0003-4558-9667
dc.identifier.scopus2-s2.0-105008416975
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ICHORA65333.2025.11017155
dc.identifier.urihttps://hdl.handle.net/11616/109130
dc.identifier.wosWOS:001533792800151
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee
dc.relation.ispartof2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Ichora
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectmigraine
dc.subjectsensory stimulus
dc.subjectcross entropy
dc.subjectconnectivity feature
dc.subjectartificial neural network
dc.titleMigraine Analysis with Cross Entropy Based Connectivity Feature: Investigation of Sensory Stimulus Conditions
dc.typeConference Object

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