A system adapted to assist with assessing susceptibility to epilepsy and/or epileptic seizures in a patient, the system including: a device configured to receive patient EEG brain data; a device configured to generate a network model from the received patient brain data from periods of time during which no seizures occur, wherein nodes in the network model correspond to brain regions of the patient brain data based on interdependencies between EEG signals from different recording electrodes or brain regions of an EEG source and connections between the nodes of the network model correspond to measured connections between the brain regions, wherein the network model is inferred from the patient data using a beta weights method; a device configured to generate synthetic brain activity data in at least some of the nodes of the network model wherein the synthetic brain activity data is generated using a dynamic computational model based on features of human seizure data that phenomenologically or physiologically models transitions from interictal to ictal states in brain regions over time; a device configured to compute seizure frequency from the synthetic brain activity data by monitoring transitions from non-seizure states to seizures states in at least some of the nodes over time; a device configured to use the seizure frequency to compute a likelihood of susceptibility to epilepsy and/or epileptic seizures in the patient, and a device configured to compare the computed likelihood with another likelihood of susceptibility to epilepsy and/or epileptic seizures computed using the same process performed on previous brain data from periods of time during which no seizures occur from the same patient in order to assess whether the likelihood has increased or decreased. a device configured to receive patient EEG brain data; a device configured to generate a network model from the received patient brain data from periods of time during which no seizures occur, wherein nodes in the network model correspond to brain regions of the patient brain data based on interdependencies between EEG signals from different recording electrodes or brain regions of an EEG source and connections between the nodes of the network model correspond to measured connections between the brain regions, wherein the network model is inferred from the patient data using a beta weights method; a device configured to generate synthetic brain activity data in at least some of the nodes of the network model wherein the synthetic brain activity data is generated using a dynamic computational model based on features of human seizure data that phenomenologically or physiologically models transitions from interictal to ictal states in brain regions over time; a device configured to compute seizure frequency from the synthetic brain activity data by monitoring transitions from non-seizure states to seizures states in at least some of the nodes over time; a device configured to use the seizure frequency to compute a likelihood of susceptibility to epilepsy and/or epileptic seizures in the patient, and a device configured to compare the computed likelihood with another likelihood of susceptibility to epilepsy and/or epileptic seizures computed using the same process performed on previous brain data from periods of time during which no seizures occur from the same patient in order to assess whether the likelihood has increased or decreased. <EOS>