The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG
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Conventional sleep electroencephalography (EEG) measures often rely on predefined bands, thresholds, and averages that incompletely capture transient oscillatory dynamics across an entire night. Here, we introduce the Dynamic Oscillation (DYNAM-O) Toolbox, an open-source, cross-platform (MATLAB, Python, and Rust) software package for data-driven characterization of individualized neural dynamics in sleep EEG. DYNAM-O identifies transient oscillations as time-frequency peaks on multitaper spectrograms using a novel multi-resolution procedure, computes intrinsic and sleep-state-dependent extrinsic features for each event, and represents the overnight distributions of tens of thousands of TF-peaks as feature histograms spanning oscillation frequency, slow oscillation power, and slow oscillation phase. This distributional representation preserves continuous brain-state variation that could be obscured by averaging within conventional sleep stages. The toolbox further provides Gaussian and spline basis-based dimensionality reduction, visualization, and whole-histogram statistical testing tools to support both exploratory and hypothesis-driven analyses. To demonstrate its use for group-level inference, we analyzed overnight C3-channel EEG from 133 adults (71 females, 72 males; ages 20–35 years) in the Cleveland Family Study. Whole-histogram and parameterized-mode analyses reproduced the established higher center frequency of fast-spindle activity in females and additionally revealed greater low-alpha transient oscillatory activity in females, a pattern outside the conventional sleep spindle range. By completing the analysis cycle from TF-peak extraction to statistical inference, DYNAM-O provides an accessible and interpretable framework for studying individualized sleep physiology and identifying subtle, reproducible electrophysiological patterns.
Statement of significance
This paper provides a full methodological introduction of the DYNAM-O Toolbox as an open-source package spanning MATLAB, Python, and Rust. This accessible toolbox enables researchers to study broad classes of transient “spindle-like” oscillations, which have been shown to be highly individualized: heterogeneous between individuals, yet robust night-to-night. This approach has immediate applications in longitudinal studies, group comparisons, and feature extraction for deep learning models. Overall, DYNAM-O is a robust, user-friendly toolbox for the quantification of individualized sleep dynamics, providing a powerful framework for clinical and experimental research in sleep, neuroscience, and beyond.
