ArticleFrontiers in neuroinformatics2026
Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.
Article in Frontiers in neuroinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Understanding the temporal organization of brain activity requires methods that capture scale-free dynamics while accounting for the high-dimensional, spatially correlated nature of the electroencephalogram (EEG) data. We propose a novel framework that integrates nonlinear manifold learning (Isometric mapping) with detrended fluctuation analysis (DFA) to quantify long-range temporal correlations (LRTC) in the alpha-band of EEG signals. We applied this framework to two music related EEG datasets, as music is known to evoke different emotions and synchronize brain activity. The first dataset was obtained during live Indian classical music (ICM) listening that included two ragas, Yaman and Puriya Dhanashree. EEG was recorded from 13 healthy volunteers (24 channels, sampled at 500 Hz). The second dataset is the Music BCI dataset (006-2015), which includes Jazz and Synth-pop musical clips, with EEG collected from 11 subjects (64 channels, downsampled to 200 Hz). The EEG data from both datasets were preprocessed, band-limited to 8-13 Hz, and segmented into non-overlapping 2-s windows. Alpha-band power was extracted from each channel to form the feature matrix used for embedding. For the ICM dataset, Isometric mapping (Isomap) produced a low-dimensional representation (
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