Distribution based EEG baseline classification

Abstract

Electrical signals generated in the brain, known as Electroen-cephalographic (EEG) signals, form a non-invasive measure of brain functioning. Baseline states of EEG are Eyes Open (EO) and Eyes Closed (EC) relaxed states. The choice of baseline used in an experiment is of critical importance since they form a reference with which other states are measured. In Brain Machine Interface, it is imperative that the system should be able to distinguish between these states and hence the need for automated classification of EEG baselines. In the proposed method, Statistical Moments are utilized. The Moment Generating Functions (MGFs) obtained using these moments are given as features to SVM and k-NN classifiers resulting in mean accuracies of 86.71% and 86.54%. The fact that MGF is able to differentiate between these states indicate that the two states have different source distribution parameters. A Smirnov test verified that the data of two classes indeed come from different distributions.