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Dynamic Temporal Alignment of EEG and Music: A Novel Framework for Real-Time Music Cognition Research

Poster Session C - Sunday, March 8, 2026, 5:00 – 7:00 pm PDT, Fairview/Kitsilano Ballroom

Oguzhan Tugral¹ (); ¹University of Massachusetts Amherst - Department of Music and Dance

Cognitive neuroscience research on music cognition seeks to understand how musical experiences shape neural dynamics, emotion, and memory, particularly in cognitive decline associated with Alzheimer’s disease and dementia. Although EEG and behavioral techniques have advanced substantially, current tools still struggle to achieve the temporal precision needed to synchronize neural and musical data streams in real time, often resulting in fragmented interpretations of the dynamic interplay between sound and brain activity. To address this challenge, the present study introduces a real-time multimodal framework that synchronizes precise musical tokens—such as pitch-class sets and Roman numerals—with EEG data through a dynamically adaptive temporal model. The system aligns neural activity and auditory input within musical time–signature–sensitive analysis windows that expand or contract according to tempo. Implemented through signal computation, the framework enables simultaneous monitoring of EEG frequency bands in relation to musical events. Results indicate that EEG band activity within each musical measure tracks listener responses to specific musical tokens—chords, bass, soprano, root —with millisecond-level precision. The system captures neural correlations of these responses in real time, allowing precise mapping of valence-related neural dynamics to musical structure. By integrating EEG, acoustic, and behavioral signals within a unified temporal framework, this tool advances experimental precision in music cognition and therapeutic neuroscience. It provides a reproducible infrastructure for examining how harmonic features engage neural mechanisms of emotion and memory, supporting future applications in music therapy and translational research in cognitive neuroscience.

Topic Area: METHODS: Electrophysiology