The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG
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…
The Dynamic Oscillation (DYNAM-O) Toolbox is an open-source software package designed for data-driven analysis of individualized neural dynamics in sleep EEG. This tool distinguishes transient oscillations as time-frequency peaks on multitaper spectrograms using a multi-resolution procedure. It then calculates intrinsic and sleep-state-dependent extrinsic features for each event and represents overnight distributions of tens of thousands of time-frequency peaks as feature histograms.
These histograms span oscillation frequency, slow oscillation power, and slow oscillation phase, preserving continuous brain-state variation that may be overlooked when averaging within conventional sleep stages.
The DYNAM-O Toolbox also includes tools for dimensionality reduction using Gaussian and spline basis, visualization, and statistical testing of whole histograms to support both exploratory and hypothesis-driven analyses. Its capabilities were demonstrated in a study analyzing overnight C3-channel EEG from 133 adults, including 71 females and 72 males aged 20-35 years, from the Cleveland Family Study.
The analysis confirmed higher center frequency of fast-spindle activity in females and uncovered greater low-alpha transient oscillatory activity in females, a pattern outside the conventional sleep spindle range. By integrating the entire analysis cycle from time-frequency peak extraction to statistical inference, the DYNAM-O Toolbox offers an accessible and interpretable framework for investigating individualized sleep physiology and identifying subtle, reproducible electrophysiological patterns.
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