Brain Computer Interface Based Smart Environment Control

Abstract

The main advantage of a Brain Computer Interfaced (BCI) systems is that it enables direct communication between brain and computer. This study proposes an Electroencephalogram (EEG) based BCI system for smart environment control. Features from EEG data, ISRUC-Sleep was extracted. Extracted features from data were used for training a classifier for classification of the cognitive stage of the person (Alert, Relaxed and Sleep). The weighted k nearest neighbor (Wk-NN) algorithm based classifier was designed on MATLAB. And the environment was controlled based on the cognitive state of the person. The accuracy achieved for classification of the cognitive state was 92.5%. At last a prototype smart environment control was practiced.