**Summary** In this project, we investigate whether resting-state EEG alpha-power differentiates between epilepsy patients with poor versus good seizure control. The alpha rhythm, originally described by Berger (1932), is a 8-13 Hz oscillatory signal predominantly seen over occipital recording sites in subjects at rest with closed eyes. Early observations in neuropsychiatric disorders suggested a spectral and spatial shift of the alpha-rhythm, i.e. a reduction of its dominant frequency to 6-8 Hz and an increase of its power over frontal sites, a phenomenon that was particularly frequent in patients with epilepsy (Stoller 1949). Several recent reports on resting-state EEG power in epilepsy are broadly in alignment with these historical findings, and collectively suggest that there is indeed a spectral shift of the alpha rhythm to lower frequencies across multiple focal and generalised epilepsy syndromes (e.g. Clemens 2000, Clemens 2004, Pyrzowski 2015). Interestingly, alterations of the alpha rhythm are also found in relatives of epilepsy patients with genetic generalised epilepsy. For instance, Doose et al. described that the parents of children with generalised epilepsy syndromes show increased alpha power over all EEG electrodes, not only occipital ones ("generalised alpha") (Doose 1995). In addition, current analyses show that the topology of EEG-derived networks is altered in the low-frequency alpha band in both patients with idiopathic generalised epilepsies and their first-degree relatives (Chowdhury 2015). A computational modelling study further found that patient-derived low-alpha networks were much more prone to generate seizure-like dynamics compared to networks derived from healthy controls (Petkov 2014). With this background in mind, we hypothesise that spatio-spectral shifts of the resting-state alpha rhythm might be a generic, i.e. syndrome-independent, marker of seizure-prone brain dynamics in epilepsy. We here test this hypothesis in n = 63 patients with focal (n = 38) and genetic generalised syndromes (n = 25), and a control croup of healthy volunteers (n = 38), using cluster-based permutation statistics of scalp-frequency data to achieve robust inference, and stastically controlling for age, gender, disease duration and medication. Wiki pages below provide more details on each component of the project.