ArticleFrontiers in neuroscience2021
An Analytical Framework of Tonal and Rhythmic Hierarchy in Natural Music Using the Multivariate Temporal Response Function.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Neural and Behavioral Tracking of Musical Phrases Occurs Without Temporal Regularity.The European journal of neuroscience · 2026Article
- Neural Mechanism of Musical Pleasure Induced by Prediction Errors: An EEG Study.Brain sciences · 2024Article
- From pre-processing to advanced dynamic modeling of pupil data.Behavior research methods · 2024Article
- Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions.eLife · 2023Article
- Inter-subject correlations of EEG reflect subjective arousal and acoustic features of music.Frontiers in human neuroscience · 2023Article
- On the encoding of natural music in computational models and human brains.Frontiers in neuroscience · 2022Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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Abstract
Even without formal training, humans experience a wide range of emotions in response to changes in musical features, such as tonality and rhythm, during music listening. While many studies have investigated how isolated elements of tonal and rhythmic properties are processed in the human brain, it remains unclear whether these findings with such controlled stimuli are generalizable to complex stimuli in the real world. In the current study, we present an analytical framework of a linearized encoding analysis based on a set of music information retrieval features to investigate the rapid cortical encoding of tonal and rhythmic hierarchies in natural music. We applied this framework to a public domain EEG dataset (OpenMIIR) to deconvolve overlapping EEG responses to various musical features in continuous music. In particular, the proposed framework investigated the EEG encoding of the following features:
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