Features extraction and classification of EEG signals using empirical mode decomposition and support vector machine

Abstract

Interpreting brain waves can be so important and useful in many ways. Having more control on your devices, helping disabled people, or just getting personalized systems that depend on your mood are only some examples of what it can be used for. An important issue in designing a brain-computer interface (BCI) is interpreting the signals. There are many different mental tasks to be considered. In this paper we focus on interpreting left, right, foot and tongue imagery tasks. We use Empirical Mode Decomposition (EMD) for feature extraction and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel for classification. We evaluate our system on the dataset 2a from BCI competition IV, and very promising classification accuracy that reached 100% is obtained.