Time domain Feature extraction and classification of EEG data for Brain Computer Interface

In the recent past Brain Computer Interface (BCI) has become popular in the field of rehabilitation engineering for physically challenged people to improve their day-to-day activities independently. A proper BCI can possibly be achieved by proper classification and feature extraction techniques from the Electroencephalogram (EEG) data acquired from the brain. In this paper time domain (TD) features, like Mean Absolute Value (MAV), Zero Crossings (ZC), Slope Sign Changes (SSC) and Waveform Length (WL) is considered for classification of six channels of EEG data with time window of size 1-sec containing 250 data with an overlap of 125 data. A pair-wise combination of five different mental tasks has been considered for classification using Linear Discriminate Analysis (LDA) for seven subjects. Classification accuracies ranging from 67%-100% is obtained for pair-wise classification. The classification accuracy with TD features is found to be considerably increased besides reduction in the memory space and processing time of the classifier used in BCI applications.