In this work , first we introduce some concepts and tools to analyze electroencephalography (EEG) data and then we try to investigate two sets of EEG data using these methods . First , we compute correlation matrix using moving statistics for increments of data set 1 as well as focal and non-focal areas separately and then we find probability density function of eigenvalues of this matrix . At last , by investigating this function for the second eigenvalue , we find that for each patient in the data set the second peak in ictal states for focal areas is higher than the one for non-focal areas . In the second part of this work , we investigate correlation states between different areas for second data set by employing a tool widely used in the field of Machine Learning namely hierarchical clustering . In this method after the computation of moving correlation matrix by using Average-Linkage clustering , we try to find correlation states and then time evolution of state of the system . By investigating plots corresponding to time evolution of states , we conclude that system state in time period of epileptic seizure is different from stable state before the seizure . In addition , correlation states for post-ictal data are very different from correlation states for other periods of time . On the other hand , by observing the change of system state in approximately 17 minutes before seizure onset , we conclude that this is an alarm for the seizure and we can use it to anticipate epileptic seizure . At last , by investigating correlation states in each time period , we can observe that focal areas are strongly correlated in all of states and this situation does not depend on the time period that we studied .