Human machine interaction via brain activity monitoring

Abstract

Brain Computer Interfaces (BC!) are becoming increasingly studied as methods for users to interact with computers because recent technological developments have lead to low priced, high precision BCI devices that are aimed at the mass market. This paper investigates the ability for using such a device in real world applications as well as limitations of such applications. The device tested in this paper is called the Emotiv EPOC headset, which is an electroencephalograph (EEG) measuring device and enables the measuring of brain activity using 14 strategically placed sensors. This paper presents: 1) a BCI framework driven completely by thought patterns, aimed at real world applications 2) a quantitative analysis of the performance of the implemented system. The Emotiv EPOC headset based BCI framework presented in this paper was tested on a problem of controlling a simple differential wheeled robot by identifying four thought patterns in the user: “neutral”, “move forward”, “turn left”, and “turn right”. The developed approach was tested on 6 individuals and the results show that while BCI control of a mobile robot is possible, precise movement required to guide a robot along a set path is difficult with the current setup. Furthermore, intense concentration is required from users to control the robot accurately.