## 2 Materials and Methods
### Impact of Head Modeling Strategies  
Several test models were created, by varying the reference analysis workflow described above with respect to a single parameter at the time. These tests were focused on assessing electrode localization errors and head tissue segmentation accuracy, respectively. For each test model, the corresponding analysis workflow was re-run on the hdEEG data, to produce new connectivity results for: (1) band-limited RSN correlation matrices and (2) the contrast between intra- and inter-network connectivity for each RSN and frequency band. Figure 1: Anatomical positions of the 21 seeds used for analysis, subdivided into the corresponding networks. Default mode network (DMN): posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), left/right angular gyrus (IANG/rANG); Dorsal attention network (DAN): left/right frontal eye field (IFFE/rFE), left/right inferior parietal sulcus (IIPS/rLPS); ventral attention network (VAN): right temporo-parietal junction (rTPD), right inferior frontal gyrus (rIFG); Language network (LN, green): left temporo-parietal junction (ITPI), left inferior frontal gyrus (IIFG); Somatomotor network (SMN): supplementary motor area (SMA), left/right primary somatosensory cortex (IS1/rS1), left/right secondary somatosensory cortex (IS2/rS2); Visual network (VN): left/right human ventral visual area 4 (IV4v/rV4v), left/right visual area 5 (IV5/rV5). To test the reliability of each model, we calculated [PERSON]'s correlation values between the connectivity matrices reconstructed from the reference analysis workflow and those obtained from the specific test model. The correlation values were transformed to z-values using the Fisher transformation to improve data normality. Generation of Test Models  
In order to evaluate the impact of electrode localization errors on the connectivity results, we created eight sets of synthetic electrode positions by adding displacements to the reference electrode positions (Figure 2). These displacements differed in magnitude and type. In particular, we defined six possible rotations around the anteroposterior, the mediolateral and the longitudinal axes of the head. The amplitude of the rotation determined the displacement of each electrode, which was defined according to previous studies [45; 48; 65], and was set to approximately: 0.25, 0.5, 0.75 and 1 cm. Within each dataset, the same direction of rotation was applied to all the electrodes to simulate the co-registration error (_\"Systematic\"_), whereas a random rotation was applied to each electrode for the localization error (_\"Random\"_). Rotations were randomized across datasets. As a second analysis step, we simulated the impact of electrode position errors that are typical of 3D scanning [43; 44; 45] and digitization techniques [39; 40]. The synthetic set of electrode positions for 3D scanning (_3D Scan_) was created by considering a systematic error equal to 0.25 cm and no random error; for the digitization one (_Digitizer_) we included a systematic error of 0.5 cm and a random error of 0.25 cm [44; 45; 65] (Table S2). The amplitude of the rotation was constant for all the datasets, while the direction of the rotation was randomized across participants. We also examined the impact of the head tissue segmentation on the connectivity results, by using three different segmentation approaches with decreasing levels of  
Figure 2: Example of EEG electrode positions and their simulated misplacement. From top to bottom: original electrode positions for a representative participant; positions with a systematic error of 1 cm; positions with a random error of 1 cm. complexity (Figure 3). First, we applied the warping of template segmentation (WTS) technique [10; 25] on the individual MR image to obtain a _\"12-layer WTS\"_ segmentation, with the same compartments defined by MR-TIM [25].