In this project, information theory, graph theory, as well as the omega complexity is presented in order to analyse dependencies in EEG signals. An analysis of the omega complexity is performed, and a generalised omega complexity is introduced to combat some of the presented deficiencies. The methods are tested on coupled Rössler systems and multivariate autoregressive processes as these have proven to be comparable with EEG signals in their behaviour. Initially an EEG data set obtained from a subject exposed to a high and low SNR environment is analysed, although no significant changes between the two are found. Next, the presented methods are applied to an iEEG data set on a subject with epilepsy, resulting in significant changes between dependencies in the EEG signals prior to a seizure and during a seizure. Hence the methods introduced are to some degree able to capture changes in dependencies in EEG signals.