An integrated classification method for brain computer interface system

Abstract

A channel of communication for both human brain and computer system is provided via a system called Brain Computer Interface (BCI). The vital aim of BCI research is to develop a system that helps the disabled people to interact with other persons and allows their interaction with the external environments or as an additional man-machine interaction channel for healthy users. Different techniques have been developed in the literature for the classification of brain signals. The purpose of this work is to deveolp a novel method of analyzing the EEG signals. We have used high pass filter to remove artifacts, DWT algorithms for feature extraction and features like Mean Absolute Value, Root Mean Square, and Simple Square Integral are used. The neural network algorithm is used to find the correct class label for EEG signal after clustering the feature vectors using K-Nearest Neighbor algorithm. It has been depicted from results that the proposed integrated technique outperforms a better performance than methods mentioned in literature.