Feature Extraction of Motor Imagery EEG Data Using Time Domain Statistical Parameters

Abstract

A brain–computer interface (BCI) provides a communication pathway between the brain and the outside world. It is a boon for people disabled by neuromuscular disorders. BCI works by measuring brain signals, analyzing, interpreting them and translating them into actions. Electroencephalography(EEG) is the measurement of electrical activity produced by the brain. Motor Imagery is the mental simulation of a kinesthetic movement without any physical movement. Each brain signal is quantified by a few relevant values known as features. Once the features are extracted the users intentions can be identified. Feature extraction module is responsible for choosing the features which are very important for classification. In this paper we propose time domain statistical feature extraction techniques such as mean correlation, Kurtosis, Skewness which are classified with KNN classifier. The results are compared with features extracted by Common Spatial Pattern (CSP) and classified\xa0 using Linear Discriminant Analysis classifier.