Electroencephalographic (EEG) signal texture content analysis has been proposed for early warning of an epileptic seizure. This approach was evaluated by investigating the interrelationship between texture features and basic signal informational characteristics, such as Kolmogorov complexity and fractal dimension. The comparison of several traditional techniques, including higher-order FIR digital filtering, chaos, autoregressive and FFT time-frequency analysis was also carried out on the same epileptic EEG recording. The purpose of this study is to investigate whether wavelet transform can be used to further enhance the developed methods for prediction of epileptic seizures. The combined consideration of texture and entropy characteristics extracted from subsignals decomposed by wavelet transform are explored for that purpose. Yet, the novel neuro-fuzzy clustering algorithm is performed on wavelet coefficients to segment given EEG recording into different stages prior to an actual seizure onset.