EEG Alpha-Band Long-Range Correlations from Two Music Listening Studies

Two EEG datasets from music listening experiments, used to analyze long-range temporal correlations in the alpha-band. The first dataset includes recordings from 13 volunteers listening to Indian classical music, and the second is a public Music BCI dataset with 11 subjects listening to Jazz and Synth-pop. The data, last updated in 2026, was analyzed using a novel framework combining nonlinear manifold learning and detrended fluctuation analysis.

Use Cases

• Benchmarking nonlinear dimensionality reduction methods for EEG based on the described Isomap and PCA pipelines.
• Investigating music-evoked brain dynamics and emotional responses using the provided alpha-band power features.
• Developing or validating detrended fluctuation analysis (DFA) techniques for quantifying long-range temporal correlations.
• Comparing neural responses across different musical genres (Indian classical, Jazz, Synth-pop) as described in the two datasets.

Strengths

• Includes two distinct EEG datasets with 13 and 11 subjects, providing a basis for method comparison.
• Analysis yielded specific scaling exponent (α) ranges (0.62–0.70) and statistical significance values (e.g., p=0.008) for music-related changes.
• Released under a permissive CC-BY-4.0 license, facilitating reuse and redistribution.

Limitations

• The dataset is very small (194.4 KB), suggesting it contains processed results or a supplementary document rather than raw EEG time-series.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for machine learning tasks.

Provenance

Source

figshare, authored by Dasari Shivakumar.

Collection Method

EEG recorded from healthy volunteers during music listening sessions, preprocessed and band-limited to 8–13 Hz.

Freshness

Last updated 2026-05-20 05:44:16; freshness should be verified.