Statistical feature analysis for EEG baseline classification: Eyes Open vs Eyes Closed

Abstract

Electroencephalographic (EEG) patterns are electrical signals generated in the brain indicating brain functioning. Due to its non-invasive nature, it has been used in applications ranging from disorder detection, sleep analysis to Brain Machine Interface. A baseline state is required in all these applications to compare the required state with a reference state. In EEG analysis, Eyes Open (EO) and Eyes Closed (EC) relaxed states are the baselines used. The choice of baseline is important especially in Brain Machine Interface. Thus the system should be able to distinguish between these two states and hence the need for automated classification of EEG Baseline States. In the proposed approach, statistical features are used in classification of these two states along with Support Vector Machine (SVM) and k-Nearest Neighbour(k-NN) classifiers. Thirteen different statistical features are considered and it was found that the combination of kurtosis, IQR and MAD with k-NN classifier (k=7) gave the mean accuracy of 77.92%. The fact that kurtosis, IQR and MAD perform better implies that the underlying distributions of the two classes have significant difference.