TAN Lee FanHuei Qhi TeohMun Hou KitKiruthika Selvakumar2026-10-192026-10-192025-10-2710.1109/TENCON66050.2025.11375138https://dspace-cris.utar.edu.my/handle/123456789/11781Migraine is a common neurological disorder that substantially affects quality of life (QoL) and is associated with altered cortical activity. While advanced neuroimaging has enhanced understanding of migraine-related brain changes, such methods are often costly and not widely accessible. This study investigates the feasibility of using a low-channel, wearable electroencephalography (EEG) device to assess migraine severity in a non-clinical population of university students. Fifty-three participants were stratified into four groups based on the Migraine Disability Assessment (MIDAS) questionnaire. Resting-state EEG data were recorded with the Muse 2 headband and processed via EEGLAB in MATLAB. Power spectral density (PSD) analysis focused on alpha (?, 8-12 Hz) and beta (?, 13-30 Hz) activity in frontal and temporal regions. Although statistical analysis using one-way ANOVA did not yield significant differences, a decreasing trend in ? and ? power was observed with increasing migraine severity. Participants also completed the Migraine-Specific Quality of Life Questionnaire (MSQv2.1), which showed no significant differences among the groups. The findings support the feasibility of using consumer-grade EEG systems for non-invasive assessment of migraine severity and provide a foundation for future studies involving larger cohorts and advanced signal analysis. © 2025 IEEE.en-USMIDASmigraineMuse 2PSD analysisquality of lifespectral poweruniversity studentsWearable EEGBiomedical signal processingBrainElectrophysiologyNeuroimagingNoninvasive medical proceduresQuality controlSignal analysisSpectral densityStudentsWearable technologyCortical activityMigraineMigraine disability assessmentNeurological disordersPower spectral density analysisQuality of lifeSpectral powerUniversity studentsWearable electroencephalographyElectroencephalographyAssessing Migraine Severity in University Students Using Low-Channel Wearable EEGtext::conference output::conference proceedings::conference paper