Recurrence quantification analysis (RQA) is a method of nonlinear data analysis for the investigation of dynamical systems. It quantifies the number and duration of recurrences of a dynamical system presented by its phase space trajectory. The recurrence quantification analysis was developed in order to quantify differently appearing recurrence plots (RPs) based on the small-scale structures therein. Recurrence plots are tools which visualize the recurrence behavior of the phase space trajectory of dynamical systems. They mostly contain single dots and lines which are parallel to the mean diagonal or which are vertical/horizontal. Lines parallel to the LOI are referred to as diagonal lines and the vertical structures as vertical lines. The lines correspond to a typical behaviour of the phase space trajectory: whereas the diagonal lines represent such segments of the phase space trajectory which run parallel for some time, the vertical lines represent segments which remain in the same phase space region for some time. RQA on EEG recordings and their sub bands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent sub bands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure (a sudden attack of illness, especially a stroke or an epileptic fit) course (Ictal). In our work we are going to Analyze EEG signal by RQA methodologies, which consist of the process of Wavelet decomposition, Reconstruction and analysis. Then the feature values are classified using ECOC classifier, which is medium of nearest neighbor Classification methodology.