In the past few years, the study of electrical activity in the brain and its interactions with the body has become popular among researchers. One of the hottest topics related to brain activity is the epileptic seizure prediction. Currently, there are several techniques on how to predict a seizure; however, most of the techniques found in research papers are just mathematical models and not system implementations. The seizure prediction approach proposed in this thesis paper is achieved using the EMOTIV Epoc+ headset, MATLAB, and LabVIEW as the analog and digital signal processing devices. In addition, this thesis project incorporates the use of the Hilbert Huang transform (HHT) method to obtain intrinsic mode functions (IMF) and instantaneous frequency components of the transform. From the IMFs, features as variation coefficient (VC) and fluctuation indexes (FI) are extracted to feed a support vector machine that classifies the EEG data as pre-ictal and non-ictal EEGs. Outstanding patterns in non-ictal and pre-ictal are observed and demonstrated by significant differences between both types of EEG signals. In other words, a classification of EEG signals according to a category can be achieved proving that an epileptic seizure prediction technology has a future in engineering and biotechnology fields.