Robust time delay estimation for measurement of ictal EEG traveling waves

Objective.Seizures are often characterized by epileptiform discharges that appear to be highly synchronous across electroencephalogram (EEG) channels, yet closer analysis reveals small time delays indicative of fast traveling waves. Microelectrode studies suggest that a possible source of these waves is the ictal wavefront, a slowly advancing boundary emitting opposing waves; attempts to extend this analysis to macroelectrodes produced conflicting theories, suggesting both static and moving radial sources. Because variability in EEG measurement and analysis may contribute to conflicting results, we systematically evaluated how signal characteristics and methodological choices affect wave direction estimates. We applied the resulting framework to human intracranial EEG (iEEG) to validate the results.Approach.We simulated iEEG data using a second-order autoregressive model, modeling coherence as a distance-weighted sum of signals. We then applied one of eight different time-delay propagation patterns at a range of wave speeds, and accuracy was assessed for multiple referencing schemes, sampling rates, and spatial resolutions. Human iEEG recordings were then analyzed to validate methodological recommendations and characterize ictal waves.Main Results.Simulations revealed that wave patterns could be measured most accurately for wave speeds <1000 mm s^-1and average iEEG coherence levels >0.4. Under these conditions, a corner electrode reference outperformed other referencing schemes. Wave estimation accuracy was highest for high sampling rates and low electrode spacing. Accurate results were obtained at 3 mm electrode spacing, but not at 9 mm spacing, which approximates standard clinical subdural grids (p< 0.01). Human iEEG exhibited a wide range of propagation patterns, including spirals, sources, and sinks which have not been previously reported.Significance.Detailed analyses of simulated and human iEEG data highlight critical methodological choices that must be considered when characterizing complex seizure wave patterns. Our findings provide a validated framework to increase the rigor of future studies.