The self-paced graz brain-computer interface: methods and applications.

Reinhold Scherer,Gert Pfurtscheller,Felix Lee,Alois Schloegl,Horst Bischof,Janez Janša

doi:10.1155/2007/79826

Abstract

We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

Highlights

A brain-computer interface (BCI) transforms electrophysiological or metabolic brain activity into control signals for applications and devices
We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery
Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal or not

Summary

Introduction

A brain-computer interface (BCI) transforms electrophysiological or metabolic brain activity into control signals for applications and devices (e.g., spelling system or neuroprosthesis). BCIs have to meet several technical requirements before they are practical alternatives to motor controlled communication devices. For the end-user, BCI systems have to carry information as quickly and accurately as needed for individual applications, have to work in most environments, and should be available without the assistance of other people (self-initiation). To fulfill these issues, the Graz group focused on the development of small and robust systems which are operated by using one or two bipolar electroencephalogram (EEG) channels only [13]. Motor imagery (MI), that is, the imagination of movements, is used as the experimental strategy

Methods

Results

Conclusion