For more than five decades, electroencephalography (EEG) has provided critical insights into the neural processes underlying music perception. This review examines the methodological progression of EEG-based music research from 1970-2025, analyzing how advancing technology and evolving theoretical frameworks have shaped the field.
Early studies, limited by basic equipment, focused on recording broad brain rhythms during passive listening. The 1990s introduced event-related potential (ERP) paradigms, allowing researchers to isolate specific neural responses to musical events. By the 2000s, investigators had identified potentially music-specific neural markers like the ERAN, though questions about their specificity remain unresolved. Recent years have seen a shift toward naturalistic, full-length music paradigms alongside sophisticated computational tools, creating new challenges in variable isolation and data interpretation.
Through analysis of over 60 landmark studies, we identify four critical methodological transitions: from passive observation to controlled ERPs; the emergence of music-specific neural components; the adoption of ecologically valid stimuli; and the integration of computational decoding approaches. This paper not only highlights methodological advancements but also critically examines interpretational challenges, including sensory versus cognitive processing debates and potential over interpretation of early findings. This research provides a foundation for future cross-cultural, developmental, and clinical applications.
To support systematic exploration of this literature, I developed an interactive database documenting 197 experimental conditions from 44 studies, available at https://eeg-music-studies-visualization.vercel.app/. The database enables filtering by experimental paradigm, musical features, EEG specifications, and participant characteristics. Full technical documentation is provided in the Appendix and on the GitHub. https://github.com/jerhsiao/eeg-music-studies-visualization.
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