Abstract Studies of intracranial EEG (iEEG) networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. iEEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal iEEG measures brain activity at baseline and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. iEEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal iEEG across two epilepsy centers to generate iEEG functional connectivity networks. 24 functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing (CAR) or bipolar re-referencing (BR). We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: 1) the introduction of spurious correlations, 2) robustness to incomplete spatial sampling, and 3) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: CAR pipelines, BR pipelines, and relative entropy-based connectivity pipelines. Relative entropy and CAR networks were more robust to incomplete electrode sampling than BR and other connectivity methods (Friedman test, Dunn-Sˇidák test p < 0.0001). BR reduced spurious correlations at non-adjacent channels better than CAR (Δ mean from machine ref = -0.36 vs -0.22), and worse in adjacent channels (Δ mean from machine ref = -0.14 vs -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (BH-corrected p < 0.05, Cohen’s d: 0.60 - 0.76). Finally, we present an interface where users can rapidly evaluate iEEG pre-processing choices to select the optimal preprocessing methods tailored to specific research questions. The choice of preprocessing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their preprocessing methods for deriving iEEG brain networks.