Irreversible functional network deformation induced by preprocessing scheme order in multivariate EEG recordings

The analysis of Electroencephalographic recordings is non-trivial, the order in which filtering and re-referencing are applied during multivariate signal preprocessing has a critical and underappreciated consequence for functional network estimation.

Using EEG sleep recordings from healthy subjects, synthetic data from controlled numerical models (Nf-Tori and IAAFT surrogates), and surrogate-based statistical testing, we provide a quantitative comparison of two preprocessing schemes that differ solely in the order of filtering and re-referencing (filtering followed by re-referencing-FR, and re-referencing followed by filtering-RF).

We show that the RF scheme induces spurious cross-correlations that increase systematically with the frequency band and are strongly modulated by the power spectral exponent β of the signal. Critically, for signals with steep power spectra (β ≥2), a regime that characterizes slow-wave sleep, epileptic post-ictal periods, anesthesia, neonatal, and patients in vegetative states, the RF scheme produces artificial correlations that are irreversible. They cannot be eliminated post-hoc even when surrogate-corrected cross-correlations (SAC-matrix) are applied. The mechanism underlying this effect is due to the high-amplitude slow-frequency components that dominate the median reference signal over extended time intervals, embedding redundant information across all channels. In contrast, the FR scheme introduces only small, systematic negative shifts in cross-correlations that are not statistically significant and can be effectively corrected using surrogate-based methods.

We therefore recommend the FR scheme combined with surrogate-corrected cross-correlations for all applications involving signals with β ≥2. Although this finding is demonstrated in EEG, it applies to any multivariate recording where spatial re-referencing and band-pass filtering are applied sequentially.