Interpretable metrics for evaluating fidelity, diversity, and privacy in synthetic EEG data

The generation of synthetic electroencephalography (EEG) data offers a promising solution to the limited availability of clinical neurophysiological recordings, particularly intracranial EEG (iEEG), where acquisition is invasive, complex, and restricted to small patient cohorts. Although generative models can produce realistic signals, progress in this field is constrained by the absence of standardized and physiologically grounded evaluation protocols. Current assessment strategies rely largely on basic statistical similarity measures and fail to capture the intrinsic properties of EEG, including non-stationarity, spectral organization, and fractal dynamics. Here, we propose a comprehensive evaluation framework for synthetic (i)EEG that integrates fidelity, diversity, and privacy within a unified and interpretable protocol. The framework combines analyses across time, frequency, time–frequency, complexity, and spatial domains using established signal-processing and nonlinear-dynamics metrics. Interpretation is grounded in principled normalization strategies and effect-size conventions, and is further summarized through composite domain-level scores that enable transparent and reproducible comparison across datasets and generative models. Implemented as the open-source Python library, \texttt{seege\((_)\)} provides a standardized and interpretable toolkit for evaluating synthetic neurophysiological data, advancing methodological rigor in generative modeling for clinical and neuroscientific research.