An extensive number of studies have entertained the idea of channel-wise analysis of iEEG using univariate and bivariate methods for seizure predictions (listed in Mormann 2005). These studies have relied on brute force signal processing of iEEG recordings using purely algorithmic approaches in order to construct a measure that would predict the ictal onset and location. The results of these studies provided strong evidence for the existence of a theoretical measure for seizure prediction and showed remarkable accuracy in seizure prediction, in some cases (REF). However, methodologically, these studies relied heavily on the model of focal brain areas initiating a seizure, a concept that has been seriously challenged in recent years (REF?). Moreover, these prediction algorithms required extensive training/tuning of their parameters using within-patient iEEG signals. Despite the initial optimism due to excellent performance of these logarithms on limited set of patients, all such detection algorithms failed when tested using rigorous statistical measures in multicenter studies (Mormann 2005 and 2007).