Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data

Objective: To introduce a new method of quantitative EEG analysis in the time domain, the error reductionratio (ERR)-causality test. To compare performance against cross-correlation and coherence withphase measures.Methods: A simulation example was used as a gold standard to assess the performance of ERR-causality,against cross-correlation and coherence. The methods were then applied to real EEG data.Results: Analysis of both simulated and real EEG data demonstrates that ERR-causality successfullydetects dynamically evolving changes between two signals, with very high time resolution, dependenton the sampling rate of the data. Our method can properly detect both linear and non-linear effects,encountered during analysis of focal and generalised seizures.Conclusions: We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisationin the linear and non-linear domains. It computes directionality of information flow withcorresponding time lags.Significance: This novel dynamic real time EEG signal analysis unveils hidden neural network interactionswith a very high time resolution. These interactions cannot be adequately resolved by the traditionalmethods of coherence and cross-correlation, which provide limited results in the presence of non-lineareffects and lack fidelity for changes appearing over small periods of time.

Clinical Neurophysiology, 125(1), 32–46. Elsevier