The brain signals are related to the human’s activity that reflected by Electroencephalogram (EEG). EEG recordings are complex signals in general that is being non-stationarity and non-linear. In last decade, this signal was studied by many researchers as EEG contains important information about the body’s activities. In this study, we proposed a new analysis and classification scheme that employs discrete wavelet transform (DWT) and Information Gain (InGain) denoted as DWT-InGain method. For the study of EEG signals, first step, DWT is applied to analyse the EEGs into frequency bands. Secondly, all bands are divided into windows, and from each window we extract most common statistical features. After that, the InGain is utilised to select the most important features. At the end, the extracted and selected features are used to feed the most popular classifier, support vector machine (SVM) to evaluate the execution of the proposed DWT-InGain scheme. This method is tested on a benchmark EEG database and obtains great results for five different epileptic EEG pairs in term of accuracy, sensitivity and specificity. The consequences of the proposed system might help the doctors, researchers and experts to reveal the epileptic seizures.